03 - Final Project - Mushroom Classification.ipynb

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    "colab": {
      "name": "Final_project.ipynb",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/JayOaks/GAIH-Study-Projects/blob/main/Final_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Mushroom classification 🍄\n",
        "\n",
        "In this project, we will use a public dataset from kaggle.com, the “Mushroom Classification” dataset, and try to figure out if a mushroom is edible or not. This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms. Each species is identified as edible or poisonous. When it comes to mushrooms, there is no simple rule for determining the edibility.\n",
        "\n",
        "We will tackle this classification problem using logistic regression, ridge classifier, decision tree, Naive Bayes, and neural networks. After comparing the results of each model, we will find out the best performing one. \n"
      ],
      "metadata": {
        "id": "hZ_D5I_TxJ3q"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing the required libraries\n",
        "\n",
        "As always we’ll start with importing the required libraries.\n",
        "\n",
        "📌 Use the keywords \"import\" and \"from\"."
      ],
      "metadata": {
        "id": "FT_i7HdyhrwC"
      }
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "5GcjifquDAtm"
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      "outputs": [],
      "source": [
        "# Import Pandas and Matplotlib\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import Label Encoder and train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Import Logistic Regression, Ridge Classifier, Decision Tree\n",
        "# Gaussian Naive Bayes, MLP Classifier and Random Forest models\n",
        "from sklearn.linear_model import LogisticRegression, RidgeClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Import Classification Report function\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset and preprocessing"
      ],
      "metadata": {
        "id": "DmMIhPlKOeF9"
      }
    },
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      "cell_type": "markdown",
      "source": [
        "![dataset-cover.jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "uvfb66EMxV5v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset includes data from 8124 mushrooms. Each of  these mushroom samples have 22 features and they are categorized as edible or poisonous."
      ],
      "metadata": {
        "id": "RkOEWMxYYCHN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Read the data\n",
        "\n",
        "Let’s read the .csv file.\n",
        "\n",
        "📌 Use the read_csv() function of the Pandas library."
      ],
      "metadata": {
        "id": "w3Oc5teBbgDF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the \"mushroom.csv\" file\n",
        "data = pd.read_csv('mushrooms.csv')"
      ],
      "metadata": {
        "id": "XtvTglxJDWGu"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Visualize the data\n",
        "Then, take a look at the dataset using *data.head()* function."
      ],
      "metadata": {
        "id": "n4p176zydqgp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the head() function to display the first 5 rows of the data\n",
        "data.head()"
      ],
      "metadata": {
        "id": "K0jvZ0qzdxqT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "outputId": "ec1cecb9-7d9f-4fba-86f4-60320beeb274"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
              "0     p         x           s         n       t    p               f   \n",
              "1     e         x           s         y       t    a               f   \n",
              "2     e         b           s         w       t    l               f   \n",
              "3     p         x           y         w       t    p               f   \n",
              "4     e         x           s         g       f    n               f   \n",
              "\n",
              "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
              "0            c         n          k  ...                        s   \n",
              "1            c         b          k  ...                        s   \n",
              "2            c         b          n  ...                        s   \n",
              "3            c         n          n  ...                        s   \n",
              "4            w         b          k  ...                        s   \n",
              "\n",
              "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
              "0                      w                      w         p          w   \n",
              "1                      w                      w         p          w   \n",
              "2                      w                      w         p          w   \n",
              "3                      w                      w         p          w   \n",
              "4                      w                      w         p          w   \n",
              "\n",
              "  ring-number ring-type spore-print-color population habitat  \n",
              "0           o         p                 k          s       u  \n",
              "1           o         p                 n          n       g  \n",
              "2           o         p                 n          n       m  \n",
              "3           o         p                 k          s       u  \n",
              "4           o         e                 n          a       g  \n",
              "\n",
              "[5 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c821531-4a02-4ead-a749-b7f62b5a04c0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>cap-shape</th>\n",
              "      <th>cap-surface</th>\n",
              "      <th>cap-color</th>\n",
              "      <th>bruises</th>\n",
              "      <th>odor</th>\n",
              "      <th>gill-attachment</th>\n",
              "      <th>gill-spacing</th>\n",
              "      <th>gill-size</th>\n",
              "      <th>gill-color</th>\n",
              "      <th>...</th>\n",
              "      <th>stalk-surface-below-ring</th>\n",
              "      <th>stalk-color-above-ring</th>\n",
              "      <th>stalk-color-below-ring</th>\n",
              "      <th>veil-type</th>\n",
              "      <th>veil-color</th>\n",
              "      <th>ring-number</th>\n",
              "      <th>ring-type</th>\n",
              "      <th>spore-print-color</th>\n",
              "      <th>population</th>\n",
              "      <th>habitat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>p</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>n</td>\n",
              "      <td>t</td>\n",
              "      <td>p</td>\n",
              "      <td>f</td>\n",
              "      <td>c</td>\n",
              "      <td>n</td>\n",
              "      <td>k</td>\n",
              "      <td>...</td>\n",
              "      <td>s</td>\n",
              "      <td>w</td>\n",
              "      <td>w</td>\n",
              "      <td>p</td>\n",
              "      <td>w</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>k</td>\n",
              "      <td>s</td>\n",
              "      <td>u</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>e</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>y</td>\n",
              "      <td>t</td>\n",
              "      <td>a</td>\n",
              "      <td>f</td>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>k</td>\n",
              "      <td>...</td>\n",
              "      <td>s</td>\n",
              "      <td>w</td>\n",
              "      <td>w</td>\n",
              "      <td>p</td>\n",
              "      <td>w</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>n</td>\n",
              "      <td>n</td>\n",
              "      <td>g</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>e</td>\n",
              "      <td>b</td>\n",
              "      <td>s</td>\n",
              "      <td>w</td>\n",
              "      <td>t</td>\n",
              "      <td>l</td>\n",
              "      <td>f</td>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>n</td>\n",
              "      <td>...</td>\n",
              "      <td>s</td>\n",
              "      <td>w</td>\n",
              "      <td>w</td>\n",
              "      <td>p</td>\n",
              "      <td>w</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>n</td>\n",
              "      <td>n</td>\n",
              "      <td>m</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>p</td>\n",
              "      <td>x</td>\n",
              "      <td>y</td>\n",
              "      <td>w</td>\n",
              "      <td>t</td>\n",
              "      <td>p</td>\n",
              "      <td>f</td>\n",
              "      <td>c</td>\n",
              "      <td>n</td>\n",
              "      <td>n</td>\n",
              "      <td>...</td>\n",
              "      <td>s</td>\n",
              "      <td>w</td>\n",
              "      <td>w</td>\n",
              "      <td>p</td>\n",
              "      <td>w</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>k</td>\n",
              "      <td>s</td>\n",
              "      <td>u</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>e</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>g</td>\n",
              "      <td>f</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>w</td>\n",
              "      <td>b</td>\n",
              "      <td>k</td>\n",
              "      <td>...</td>\n",
              "      <td>s</td>\n",
              "      <td>w</td>\n",
              "      <td>w</td>\n",
              "      <td>p</td>\n",
              "      <td>w</td>\n",
              "      <td>o</td>\n",
              "      <td>e</td>\n",
              "      <td>n</td>\n",
              "      <td>a</td>\n",
              "      <td>g</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c821531-4a02-4ead-a749-b7f62b5a04c0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8c821531-4a02-4ead-a749-b7f62b5a04c0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8c821531-4a02-4ead-a749-b7f62b5a04c0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, to have a better understanding of the dataset, we can utilize some visualization techniques. For example, by creating a bar graph, we can compare the different classes.\n",
        "\n",
        "We’ll start with finding the number of samples per class.\n",
        "\n",
        "📌 Use the value_counts() method."
      ],
      "metadata": {
        "id": "f9O3u7geeWYY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use value_counts method on \"class\" column of data object\n",
        "classes = data['class'].value_counts()\n",
        "\n",
        "# Print the result\n",
        "classes"
      ],
      "metadata": {
        "id": "rnWcuoTz3QWd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec1ccc62-756f-437a-ead0-9c0c6448a65f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "e    4208\n",
              "p    3916\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "With this information, we can create bars for each class and display the graph.\n",
        "\n",
        "📌 Use the .bar() method to create the graph.\n",
        "\n",
        "📌 Don't forget to use plt.show()."
      ],
      "metadata": {
        "id": "RZxbSpikx7Pk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Add the bar for edible class\n",
        "plt.bar('Edible', classes['e'])\n",
        "\n",
        "# Add the bar for poisonous class\n",
        "plt.bar('Poisonous', classes['p'])\n",
        "\n",
        "# Print the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "w6JsyP8FeWJy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "555900c4-e633-4d4e-cc39-b94d87da9c88"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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x9CWpI4a+JHXE0Jekjiwo9JN8J8mDSXYmmWi1k5PsSPJ4+3lSqyfJZ5JMJnkgyTsX4wAkSaNbjDP9n62qNVU13tY3AbdX1Wrg9rYOcBGwuj02ANctwr4lSfNwJC7vrAO2tuWtwCVD9Rtr4C7gxCSnHYH9S5JmsNDQL+BPktyXZEOrnVpVe9vyd4FT2/Jy4Omhvrtb7W9IsiHJRJKJqampBQ5PkjRszonR5/AzVbUnyd8GdiR5dHhjVVWSms8TVtVmYDPA+Pj4vPpKkma3oDP9qtrTfj4L/BFwNvDMwcs27eezrfkeYOVQ9xWtJklaIocd+kl+IskbDy4DFwAPAduB9a3ZeuDmtrwd+FC7i+dc4Pmhy0CSpCWwkMs7pwJ/lOTg8/z3qvpaknuBbUkuB54CPtja3wpcDEwCLwIfXsC+JUmH4bBDv6qeAH5ymvr3gfOnqRew8XD3J0laOD+RK0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyJKHfpK1SR5LMplk01LvX5J6tqShn2QZ8FngIuBM4LIkZy7lGCSpZ0t9pn82MFlVT1TV/wNuAtYt8RgkqVsLmRj9cCwHnh5a3w2cM9wgyQZgQ1t9IcljSzS217pTgO8d7UG8UuRTR3sEmoav0WEfy0J6/72ZNix16M+pqjYDm4/2OF5rkkxU1fjRHoc0E1+jS2OpL+/sAVYOra9oNUnSEljq0L8XWJ3k9CTHAZcC25d4DJLUrSW9vFNVB5JcAdwGLAO2VNWupRxDx7xkplc6X6NLIFV1tMcgSVoifiJXkjpi6EtSRwz9V5kkLyXZOfR42VdZJHl3klva8nsPtklyQ5L3z9Zems3Q6++hJH+Q5G/N0va9ftXKK88r7j59zekvq2rNqI2rajveIaXF8+PXX5IvAh8BPj1dQ197r0ye6b9GtC+yezTJt4BfGKr/cpL/OtT0nyeZSPK/k/z8NM/zE0m2JLknyf1J/JoMzeTPgLcmOTnJHyd5IMldSf4x/M3XXpIPtP8Ovp3kG612QpLfTfJge6397FC/ryT5WpLHk/zHgztMcllr/1Dy15+rTvLC0PL7k9ww035755n+q8/rk+wcWv8PwM3A54HzgEngS7P0X8XgO5DOAO5M8tZDtv974I6q+pdJTgTuSfKnVfV/F+sA9OqX5BgGX5z4NeBjwP1VdUmS84AbgUP/G/0N4MKq2tNeVwAbgaqqf5Tk7cCfJHlb27YGOAvYDzyW5L8ALwGfAn4KeK61v6Sq/niWoU633655pv/q85dVtWbo8SXg7cCTVfV4De7B/b1Z+m+rqr+qqseBJ1rfYRcAm9oflq8DJwB/d/EPQ69SB086JoA/B64Hfgb4AkBV3QG8JcmbDun3v4AbkvwrBp/RofX7vdbvUeAp4GDo315Vz1fVj4CHGXyXzE8DX6+qqao6AHwR+KdzjHe6/XbNM/3+HPrBjEPXA/yLqvKL7jSdl72nlMz9xWBV9ZEk5wDvAe5L8lNzdNk/tPwSc2fV8Ov4hNn2W1Xfn3PAr2Ge6b82PAqsSnJGW79slrYfSPK61vbvA4eG+23Av077TU5y1qKPVq81fwb8IgzuBAO+V1U/HG6Q5IyquruqfgOYYvAdXMP93sbgP8rZTjbuAf5ZklPa3ByXAf+zbXsmyT9I8jrgfXPst2ue6b/6HHpN/2tVtal9JfVXk7zI4JfpjTP0/3MGvzxvAj5SVT865EztE8BvAw+0X6AngZe94SsNuRrYkuQB4EVg/TRt/lOS1Qz+k7wd+DaDk5XrkjwIHAB+uar2z/SfQ1XtbbeA3tme56tVdXPbvAm4hUGwTwBvmGW/XfNrGCSpI17ekaSOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI/8fKgCx3U74/l0AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Features and labels\n",
        "Great, we have a better understanding of our data. Now we’ll divide it into features and corresponding labels. \n",
        "\n",
        "In our case we’ll use the columns “cap-shape”, “cap-color”, “ring-number” and “ring-type” as features.\n",
        "\n",
        "📌 Use the .loc() method to create X and y datasets."
      ],
      "metadata": {
        "id": "unhiru9xyAJV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the X variable for features\n",
        "X = data.loc[:, ['cap-shape', 'cap-color', 'ring-number', 'ring-type']]\n",
        "\n",
        "# Create the y variable for output labels\n",
        "y = data.loc[:, 'class']"
      ],
      "metadata": {
        "id": "-jk2yv6IDDEF"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Converting the values \n",
        "\n",
        "The values are in string format. We need to convert them to integer values to be able to perform mathematical operations with them. We’ll use label encoding for this.\n",
        "\n",
        "📌 Since the X-data has multiple columns, do this in a for loop so that you can update all columns at once.\n",
        "\n",
        "📌 For the y data, use the encoder directly."
      ],
      "metadata": {
        "id": "jSj1_gqOht6z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an LabelEncoder object\n",
        "encoder = LabelEncoder()\n",
        "\n",
        "# Encode the features to integers inside a for loop\n",
        "for i in X.columns:\n",
        "  X[i] = encoder.fit_transform(X[i])\n",
        "\n",
        "# Encode the ouput labels to integers\n",
        "y = encoder.fit_transform(y)"
      ],
      "metadata": {
        "id": "JAFSBr3Mg4sa"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s print both X and y to see the final data."
      ],
      "metadata": {
        "id": "Ak6EkKy_0n1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Print X\n",
        "X"
      ],
      "metadata": {
        "id": "KEnlMCpH8ojs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "92330dfa-1845-4147-9c98-fca0e8b9b830"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      cap-shape  cap-color  ring-number  ring-type\n",
              "0             5          4            1          4\n",
              "1             5          9            1          4\n",
              "2             0          8            1          4\n",
              "3             5          8            1          4\n",
              "4             5          3            1          0\n",
              "...         ...        ...          ...        ...\n",
              "8119          3          4            1          4\n",
              "8120          5          4            1          4\n",
              "8121          2          4            1          4\n",
              "8122          3          4            1          0\n",
              "8123          5          4            1          4\n",
              "\n",
              "[8124 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8400fd00-b75e-4266-8425-5583b7cbb4f2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cap-shape</th>\n",
              "      <th>cap-color</th>\n",
              "      <th>ring-number</th>\n",
              "      <th>ring-type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8119</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8120</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8121</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8122</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8123</th>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8124 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8400fd00-b75e-4266-8425-5583b7cbb4f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8400fd00-b75e-4266-8425-5583b7cbb4f2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8400fd00-b75e-4266-8425-5583b7cbb4f2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Print y\n",
        "y"
      ],
      "metadata": {
        "id": "D_d9KXhW8o56",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab0680fb-8a4a-43ee-b084-50538f3462f9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Split the data\n",
        "Finally, we can split our data into training and test datasets.\n",
        "\n",
        "📌 Use the train_test_split function from sklearn."
      ],
      "metadata": {
        "id": "Hf9bgEad8uRw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into train and test sets with 70-30 ratio\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "WgnpmzXg8f4W"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating and training models\n",
        "\n",
        "Our data is ready to be used! Let’s move on to training on comparing our models.\n",
        "\n",
        "We use the models we have imported already.\n",
        "\n",
        "📌 Use the relevant class names to create models."
      ],
      "metadata": {
        "id": "7EBMpKKp1IGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an object using the LogisticRegression() class\n",
        "logistic_classifier_model = LogisticRegression()\n",
        "\n",
        "# Create an object using the RidgeClassifier() class\n",
        "ridge_classifier_model = RidgeClassifier()\n",
        "\n",
        "# Create an object using the DecisionTreeClassifier() class\n",
        "decision_tree_model = DecisionTreeClassifier()\n",
        "\n",
        "# Create an object using the GaussianNB() class\n",
        "naive_bayes_model = GaussianNB()\n",
        "\n",
        "# Create an object using the MLPClassifier() class\n",
        "neural_network_model = MLPClassifier()"
      ],
      "metadata": {
        "id": "RlsX6cyo0tqW"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, we train all models with the X_train and y_train dataset we created.\n",
        "\n",
        "📌 Train all models using .fit() method of each object."
      ],
      "metadata": {
        "id": "O3LxQiOayaQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the Logistic Classifier model\n",
        "logistic_classifier_model.fit(X_train, y_train)\n",
        "\n",
        "# Train the Ridge Classifier model\n",
        "ridge_classifier_model.fit(X_train, y_train)\n",
        "\n",
        "# Train the Decision Tree model\n",
        "decision_tree_model.fit(X_train, y_train)\n",
        "\n",
        "# Train the Naive Bayes model\n",
        "naive_bayes_model.fit(X_train, y_train)\n",
        "\n",
        "# Train the Neural Network model\n",
        "neural_network_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "TyH-156D8Kh9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05c03b48-3dfb-4a9a-fe8d-7af571059ce1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the X_test set we make predictions with each model and save results to corresponding variables.\n",
        "\n",
        "📌 Use the .predict() method on each model"
      ],
      "metadata": {
        "id": "TF-4wgpxycMX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make prediction using the test dataset on Logistic Classifier model\n",
        "logistic_pred = logistic_classifier_model.predict(X_test)\n",
        "\n",
        "# Make prediction using the test dataset on Ridge Classifier model\n",
        "ridge_pred = ridge_classifier_model.predict(X_test)\n",
        "\n",
        "# Make prediction using the test dataset on Decision Tree model\n",
        "tree_pred = decision_tree_model.predict(X_test)\n",
        "\n",
        "# Make prediction using the test dataset on Naive Bayes model\n",
        "naive_bayes_pred = naive_bayes_model.predict(X_test)\n",
        "\n",
        "# Make prediction using the test dataset on Neural Network model\n",
        "neural_network_pred = neural_network_model.predict(X_test)"
      ],
      "metadata": {
        "id": "6Trj5Aws8N7z"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Comparing the performances\n",
        "\n",
        "Instead of calculating precision, recall, f-1 score and accuracy separately we can create a report to compare the performances.\n",
        "\n",
        "📌 classification_report() function is the one you have to use.\n",
        "\n",
        "📌 Print the results of all models."
      ],
      "metadata": {
        "id": "_4j6ox8Uyc9h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Classification Report for Logistic Classifier model\n",
        "logistic_report = classification_report(y_test, logistic_pred)\n",
        "\n",
        "# Create a Classification Report for Ridge Classifier model\n",
        "ridge_report = classification_report(y_test, ridge_pred)\n",
        "\n",
        "# Create a Classification Report for Decision Tree model\n",
        "tree_report = classification_report(y_test, tree_pred)\n",
        "\n",
        "# Create a Classification Report for Naive Bayes model\n",
        "naive_bayes_report = classification_report(y_test, naive_bayes_pred)\n",
        "\n",
        "# Create a Classification Report for Neural Network model\n",
        "neural_network_report = classification_report(y_test, neural_network_pred)"
      ],
      "metadata": {
        "id": "kXtgW6J_dvqB"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the report of the Logistic Regression model\n",
        "print('***** Logistic Regression *****')\n",
        "print(logistic_report)\n",
        "\n",
        "# Print the report of the Ridge Regression model\n",
        "print('***** Ridge Regression *****')\n",
        "print(ridge_report)\n",
        "\n",
        "# Print the report of the Decision Tree model\n",
        "print('***** Decision Tree *****')\n",
        "print(tree_report)\n",
        "\n",
        "# Print the report of the Naive Bayes model\n",
        "print('***** Naive Bayes *****')\n",
        "print(naive_bayes_report)\n",
        "\n",
        "# Print the report of the Neural Network model\n",
        "print('***** Neural Network *****')\n",
        "print(neural_network_report)"
      ],
      "metadata": {
        "id": "tvodE4-eKO4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b105e1e3-f821-445a-b8e8-88c785a89c75"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "***** Logistic Regression *****\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.80      0.80      1265\n",
            "           1       0.78      0.80      0.79      1173\n",
            "\n",
            "    accuracy                           0.80      2438\n",
            "   macro avg       0.80      0.80      0.80      2438\n",
            "weighted avg       0.80      0.80      0.80      2438\n",
            "\n",
            "***** Ridge Regression *****\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.80      0.80      1265\n",
            "           1       0.78      0.80      0.79      1173\n",
            "\n",
            "    accuracy                           0.80      2438\n",
            "   macro avg       0.80      0.80      0.80      2438\n",
            "weighted avg       0.80      0.80      0.80      2438\n",
            "\n",
            "***** Decision Tree *****\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.89      0.91      1265\n",
            "           1       0.88      0.92      0.90      1173\n",
            "\n",
            "    accuracy                           0.90      2438\n",
            "   macro avg       0.90      0.91      0.90      2438\n",
            "weighted avg       0.91      0.90      0.90      2438\n",
            "\n",
            "***** Naive Bayes *****\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.80      0.81      1265\n",
            "           1       0.79      0.80      0.79      1173\n",
            "\n",
            "    accuracy                           0.80      2438\n",
            "   macro avg       0.80      0.80      0.80      2438\n",
            "weighted avg       0.80      0.80      0.80      2438\n",
            "\n",
            "***** Neural Network *****\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.89      0.89      1265\n",
            "           1       0.88      0.88      0.88      1173\n",
            "\n",
            "    accuracy                           0.88      2438\n",
            "   macro avg       0.88      0.88      0.88      2438\n",
            "weighted avg       0.88      0.88      0.88      2438\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Evaluation \n",
        "\n",
        "Decision tree performed best. So maybe we can take things one step further and try the Random Forest algorithm to see if it works better. \n",
        "\n",
        "📌 Follow the same steps and print the classification report for Random Forest"
      ],
      "metadata": {
        "id": "WSJbaNlXylVz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Random Forest Classifier object, train it and make predicitons\n",
        "random_forest_model = RandomForestClassifier()\n",
        "random_forest_model.fit(X_train, y_train)\n",
        "random_forest_pred = random_forest_model.predict(X_test)"
      ],
      "metadata": {
        "id": "u09yEj7CjKUV"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a classification Report for Random Forest model\n",
        "random_forest_report = classification_report(y_test, random_forest_pred)\n",
        "\n",
        "# Print the classification report\n",
        "print(random_forest_report)"
      ],
      "metadata": {
        "id": "HoSIXhZlkC58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f42bfdc6-13d6-4968-cf1e-b60bcdd634f5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.89      0.91      1265\n",
            "           1       0.88      0.92      0.90      1173\n",
            "\n",
            "    accuracy                           0.90      2438\n",
            "   macro avg       0.90      0.91      0.90      2438\n",
            "weighted avg       0.91      0.90      0.90      2438\n",
            "\n"
          ]
        }
      ]
    }
  ]
}