diff --git a/Machine_Learning/Mushroom Classification/Mushroom Classifier (KNN, RandomForest, LogisticRegression).ipynb b/Machine_Learning/Mushroom Classification/Mushroom Classifier (KNN, RandomForest, LogisticRegression).ipynb
new file mode 100644
index 000000000..320ce1dae
--- /dev/null
+++ b/Machine_Learning/Mushroom Classification/Mushroom Classifier (KNN, RandomForest, LogisticRegression).ipynb	
@@ -0,0 +1,3315 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyP2pvNFV8fAw3l1+ZrdizAt",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/archangel2006/Machine-Learning-Models/blob/main/Mushroom%20Classifier%20(KNN%2C%20RandomForest%2C%20LogisticRegression).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🍄 Mushroom Classifier: Edible or Poisonous?"
+      ],
+      "metadata": {
+        "id": "x3H4tIaWN8fd"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "- K-Nearest Neighbors (KNN)\n",
+        "- Logistic Regression\n",
+        "- Random Forest Classifier"
+      ],
+      "metadata": {
+        "id": "VrUn8zLBOAtV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ],
+      "metadata": {
+        "id": "zWIODkppG-r1"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "chGaoiWoGvIu"
+      },
+      "outputs": [],
+      "source": [
+        "df = pd.read_csv('/content/mushrooms.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ikb8KsvgHFzQ",
+        "outputId": "a8d3ee49-badd-4510-8929-577944e9a5d5"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8124, 23)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uGIpoxy2HJMg",
+        "outputId": "7ef5cb42-72c7-4b6d-b1b5-4da391d764f5"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
+              "       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
+              "       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n",
+              "       'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
+              "       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n",
+              "       'ring-type', 'spore-print-color', 'population', 'habitat'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "g0UOlzizHJOz",
+        "outputId": "10df1683-78d3-4f34-a03c-cf89948745c3"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 8124 entries, 0 to 8123\n",
+            "Data columns (total 23 columns):\n",
+            " #   Column                    Non-Null Count  Dtype \n",
+            "---  ------                    --------------  ----- \n",
+            " 0   class                     8124 non-null   object\n",
+            " 1   cap-shape                 8124 non-null   object\n",
+            " 2   cap-surface               8124 non-null   object\n",
+            " 3   cap-color                 8124 non-null   object\n",
+            " 4   bruises                   8124 non-null   object\n",
+            " 5   odor                      8124 non-null   object\n",
+            " 6   gill-attachment           8124 non-null   object\n",
+            " 7   gill-spacing              8124 non-null   object\n",
+            " 8   gill-size                 8124 non-null   object\n",
+            " 9   gill-color                8124 non-null   object\n",
+            " 10  stalk-shape               8124 non-null   object\n",
+            " 11  stalk-root                8124 non-null   object\n",
+            " 12  stalk-surface-above-ring  8124 non-null   object\n",
+            " 13  stalk-surface-below-ring  8124 non-null   object\n",
+            " 14  stalk-color-above-ring    8124 non-null   object\n",
+            " 15  stalk-color-below-ring    8124 non-null   object\n",
+            " 16  veil-type                 8124 non-null   object\n",
+            " 17  veil-color                8124 non-null   object\n",
+            " 18  ring-number               8124 non-null   object\n",
+            " 19  ring-type                 8124 non-null   object\n",
+            " 20  spore-print-color         8124 non-null   object\n",
+            " 21  population                8124 non-null   object\n",
+            " 22  habitat                   8124 non-null   object\n",
+            "dtypes: object(23)\n",
+            "memory usage: 1.4+ MB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
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+          "height": 250
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+        "id": "PaF1a3tAHQKf",
+        "outputId": "740a888d-6b11-4ad6-c501-97064bf26a48"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       class cap-shape cap-surface cap-color bruises  odor gill-attachment  \\\n",
+              "count   8124      8124        8124      8124    8124  8124            8124   \n",
+              "unique     2         6           4        10       2     9               2   \n",
+              "top        e         x           y         n       f     n               f   \n",
+              "freq    4208      3656        3244      2284    4748  3528            7914   \n",
+              "\n",
+              "       gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
+              "count          8124      8124       8124  ...                     8124   \n",
+              "unique            2         2         12  ...                        4   \n",
+              "top               c         b          b  ...                        s   \n",
+              "freq           6812      5612       1728  ...                     4936   \n",
+              "\n",
+              "       stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
+              "count                    8124                   8124      8124       8124   \n",
+              "unique                      9                      9         1          4   \n",
+              "top                         w                      w         p          w   \n",
+              "freq                     4464                   4384      8124       7924   \n",
+              "\n",
+              "       ring-number ring-type spore-print-color population habitat  \n",
+              "count         8124      8124              8124       8124    8124  \n",
+              "unique           3         5                 9          6       7  \n",
+              "top              o         p                 w          v       d  \n",
+              "freq          7488      3968              2388       4040    3148  \n",
+              "\n",
+              "[4 rows x 23 columns]"
+            ],
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+              "      <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",
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+              "      <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",
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+              "      <th>spore-print-color</th>\n",
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+              "      <td>8124</td>\n",
+              "      <td>8124</td>\n",
+              "      <td>8124</td>\n",
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+              "      <td>8124</td>\n",
+              "      <td>8124</td>\n",
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+              "    <tr>\n",
+              "      <th>unique</th>\n",
+              "      <td>2</td>\n",
+              "      <td>6</td>\n",
+              "      <td>4</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2</td>\n",
+              "      <td>9</td>\n",
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+              "      <td>9</td>\n",
+              "      <td>9</td>\n",
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+              "      <td>4</td>\n",
+              "      <td>3</td>\n",
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+              "      <td>9</td>\n",
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+              "      <td>b</td>\n",
+              "      <td>b</td>\n",
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+              "      <td>w</td>\n",
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+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-04124e08-dad7-4ad3-95cf-ce6df5680b99 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Warning: Total number of columns (23) exceeds max_columns (20) limiting to first (20) columns.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 307
+        },
+        "id": "AJXzsaMrHS4t",
+        "outputId": "17b70d92-d65f-4de2-c512-9cfdff4307ba"
+      },
+      "execution_count": 10,
+      "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-c36d5d82-c3c7-4c1d-99aa-f13cb3dc33b5\" 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",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c36d5d82-c3c7-4c1d-99aa-f13cb3dc33b5')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-c36d5d82-c3c7-4c1d-99aa-f13cb3dc33b5 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-c36d5d82-c3c7-4c1d-99aa-f13cb3dc33b5');\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",
+              "\n",
+              "\n",
+              "    <div id=\"df-d393fc8a-9783-4875-9c9d-f4db705c4086\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d393fc8a-9783-4875-9c9d-f4db705c4086')\"\n",
+              "                title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d393fc8a-9783-4875-9c9d-f4db705c4086 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PnR3Iy1KHiGP",
+        "outputId": "2a2fb06e-32df-4e62-9cff-7d17354fc501"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 805
+        },
+        "id": "RrR9xh24HmuM",
+        "outputId": "8ef59710-3817-45af-a2fb-727f79d327df"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "class                       0\n",
+              "cap-shape                   0\n",
+              "cap-surface                 0\n",
+              "cap-color                   0\n",
+              "bruises                     0\n",
+              "odor                        0\n",
+              "gill-attachment             0\n",
+              "gill-spacing                0\n",
+              "gill-size                   0\n",
+              "gill-color                  0\n",
+              "stalk-shape                 0\n",
+              "stalk-root                  0\n",
+              "stalk-surface-above-ring    0\n",
+              "stalk-surface-below-ring    0\n",
+              "stalk-color-above-ring      0\n",
+              "stalk-color-below-ring      0\n",
+              "veil-type                   0\n",
+              "veil-color                  0\n",
+              "ring-number                 0\n",
+              "ring-type                   0\n",
+              "spore-print-color           0\n",
+              "population                  0\n",
+              "habitat                     0\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cap-shape</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cap-surface</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cap-color</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>bruises</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>odor</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gill-attachment</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gill-spacing</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gill-size</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gill-color</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>stalk-shape</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>stalk-root</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>stalk-surface-above-ring</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>stalk-surface-below-ring</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>stalk-color-above-ring</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>stalk-color-below-ring</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>veil-type</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>veil-color</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ring-number</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ring-type</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>spore-print-color</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>habitat</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Unique values per feature\n",
+        "\n",
+        "for col in df.columns:\n",
+        "    print(f\"{col}: {df[col].unique()}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cc0hWxu5Hoqo",
+        "outputId": "5d7d7223-b6e4-4194-cf6b-407b9ed3424f"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "class: ['p' 'e']\n",
+            "cap-shape: ['x' 'b' 's' 'f' 'k' 'c']\n",
+            "cap-surface: ['s' 'y' 'f' 'g']\n",
+            "cap-color: ['n' 'y' 'w' 'g' 'e' 'p' 'b' 'u' 'c' 'r']\n",
+            "bruises: ['t' 'f']\n",
+            "odor: ['p' 'a' 'l' 'n' 'f' 'c' 'y' 's' 'm']\n",
+            "gill-attachment: ['f' 'a']\n",
+            "gill-spacing: ['c' 'w']\n",
+            "gill-size: ['n' 'b']\n",
+            "gill-color: ['k' 'n' 'g' 'p' 'w' 'h' 'u' 'e' 'b' 'r' 'y' 'o']\n",
+            "stalk-shape: ['e' 't']\n",
+            "stalk-root: ['e' 'c' 'b' 'r' '?']\n",
+            "stalk-surface-above-ring: ['s' 'f' 'k' 'y']\n",
+            "stalk-surface-below-ring: ['s' 'f' 'y' 'k']\n",
+            "stalk-color-above-ring: ['w' 'g' 'p' 'n' 'b' 'e' 'o' 'c' 'y']\n",
+            "stalk-color-below-ring: ['w' 'p' 'g' 'b' 'n' 'e' 'y' 'o' 'c']\n",
+            "veil-type: ['p']\n",
+            "veil-color: ['w' 'n' 'o' 'y']\n",
+            "ring-number: ['o' 't' 'n']\n",
+            "ring-type: ['p' 'e' 'l' 'f' 'n']\n",
+            "spore-print-color: ['k' 'n' 'u' 'h' 'w' 'r' 'o' 'y' 'b']\n",
+            "population: ['s' 'n' 'a' 'v' 'y' 'c']\n",
+            "habitat: ['u' 'g' 'm' 'd' 'p' 'w' 'l']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Class distribution\n",
+        "\n",
+        "df['class'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "id": "fQdUFv8UHJRM",
+        "outputId": "1476e19d-2911-48ba-fbed-c78c9e7d8b07"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "class\n",
+              "e    4208\n",
+              "p    3916\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>e</th>\n",
+              "      <td>4208</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>p</th>\n",
+              "      <td>3916</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualize class distribution\n",
+        "\n",
+        "plt.figure(figsize=(2,2))\n",
+        "sns.countplot(data=df, x='class',hue='class', palette='Set2')\n",
+        "plt.title('Edible vs Poisonous Mushrooms')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256
+        },
+        "id": "REOr2IObHJUn",
+        "outputId": "0d46a40d-0b3c-4278-a18e-dd3454292b83"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 200x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cols_to_plot = ['odor', 'gill-color', 'cap-shape', 'gill-size', 'habitat']\n",
+        "for col in cols_to_plot:\n",
+        "    plt.figure(figsize=(3,3))\n",
+        "    sns.countplot(data=df, x=col, hue='class')\n",
+        "    plt.title(f\"{col}\")\n",
+        "    plt.xticks(rotation=45)\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "rKRhbko5IETc",
+        "outputId": "e72b8478-5e23-46d4-fa04-dda4535b7b99"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Preprocessing"
+      ],
+      "metadata": {
+        "id": "hyN_IjgTJJZ3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder"
+      ],
+      "metadata": {
+        "id": "S7FCGiGSJLTT"
+      },
+      "execution_count": 30,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Encode all categorical features\n",
+        "label_encoders = {}\n",
+        "for column in df.columns:\n",
+        "    le = LabelEncoder()\n",
+        "    df[column] = le.fit_transform(df[column])\n",
+        "    label_encoders[column] = le"
+      ],
+      "metadata": {
+        "id": "sXXeAOaZJIu9"
+      },
+      "execution_count": 31,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Split - Traning & Test Data"
+      ],
+      "metadata": {
+        "id": "VhdKtHP3JAHS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split"
+      ],
+      "metadata": {
+        "id": "JFyOk3nXJF3v"
+      },
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Split data\n",
+        "\n",
+        "x = df.drop('class', axis=1)\n",
+        "y = df['class']\n",
+        "\n",
+        "\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n"
+      ],
+      "metadata": {
+        "id": "bIy6KCutJXBr"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Scale features for KNN\n",
+        "\n",
+        "from sklearn.preprocessing import StandardScaler"
+      ],
+      "metadata": {
+        "id": "Iy2TrwRKJhiO"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scaler = StandardScaler()\n",
+        "x_train_scaled = scaler.fit_transform(x_train)\n",
+        "x_test_scaled = scaler.transform(x_test)"
+      ],
+      "metadata": {
+        "id": "54zqknNvJnGt"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Model Training"
+      ],
+      "metadata": {
+        "id": "GcFsESZbI4hG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train models\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "knn = KNeighborsClassifier()\n",
+        "logreg = LogisticRegression(max_iter=2000)\n",
+        "rf = RandomForestClassifier(random_state=42)"
+      ],
+      "metadata": {
+        "id": "u-pKby0HHF_F"
+      },
+      "execution_count": 43,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "knn.fit(x_train_scaled, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 79
+        },
+        "id": "HVfnLToWJsJ_",
+        "outputId": "89d4e06b-900d-42c2-8ba7-9887ac537d64"
+      },
+      "execution_count": 48,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "KNeighborsClassifier()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-6 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-6 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-6 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-6 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-6 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KNeighborsClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "logreg.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 79
+        },
+        "id": "NN9E8hcBJ6vm",
+        "outputId": "52b0ff3e-b2be-4d2c-ce34-a5e11644d15c"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression(max_iter=2000)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-4 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-4 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-4 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-4 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=2000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=2000)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rf.fit(x_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 79
+        },
+        "id": "l5LIU5kzHGCn",
+        "outputId": "ead0c5a9-0b9c-4f16-a6e8-5cf761f1df31"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-5 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-5 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-5 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-5 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Evaluation"
+      ],
+      "metadata": {
+        "id": "1645wWApKE7P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, classification_report\n",
+        "\n",
+        "models = {'KNN': knn, 'Logistic Regression': logreg, 'Random Forest': rf}\n",
+        "x_tests = {'KNN': x_test_scaled, 'Logistic Regression': x_test, 'Random Forest': x_test}"
+      ],
+      "metadata": {
+        "id": "NJfVovGdKGRT"
+      },
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for name, model in models.items():\n",
+        "    y_pred = model.predict(x_tests[name])\n",
+        "    print(f\"--- {name} ---\")\n",
+        "    print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+        "    print(\"Precision:\", precision_score(y_test, y_pred))\n",
+        "    print(\"Recall:\", recall_score(y_test, y_pred))\n",
+        "    print(\"F1-Score:\", f1_score(y_test, y_pred))\n",
+        "    print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n",
+        "    print()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PTulQFI8KNL2",
+        "outputId": "96b2775b-b79d-4338-bf3d-1a264b8980ae"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--- KNN ---\n",
+            "Accuracy: 1.0\n",
+            "Precision: 1.0\n",
+            "Recall: 1.0\n",
+            "F1-Score: 1.0\n",
+            "Confusion Matrix:\n",
+            " [[843   0]\n",
+            " [  0 782]]\n",
+            "\n",
+            "--- Logistic Regression ---\n",
+            "Accuracy: 0.9476923076923077\n",
+            "Precision: 0.9439490445859873\n",
+            "Recall: 0.9475703324808185\n",
+            "F1-Score: 0.9457562220804084\n",
+            "Confusion Matrix:\n",
+            " [[799  44]\n",
+            " [ 41 741]]\n",
+            "\n",
+            "--- Random Forest ---\n",
+            "Accuracy: 1.0\n",
+            "Precision: 1.0\n",
+            "Recall: 1.0\n",
+            "F1-Score: 1.0\n",
+            "Confusion Matrix:\n",
+            " [[843   0]\n",
+            " [  0 782]]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Feature Importance\n",
+        "\n",
+        "importances = rf.feature_importances_\n",
+        "features = x.columns\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.barplot(x=importances, y=features)\n",
+        "plt.title('Feature Importance from Random Forest')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 496
+        },
+        "id": "i13LZV72KZV-",
+        "outputId": "f303c423-a1d5-4b0a-9c18-3d27690720d6"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Comparision"
+      ],
+      "metadata": {
+        "id": "5XsfVnQbKWta"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "| Metric               | **KNN**                 | **Logistic Regression**   | **Random Forest**       |\n",
+        "| -------------------- | ----------------------- | ------------------------- | ----------------------- |\n",
+        "| **Accuracy**         | 1.000                   | 0.9477                    | 1.000                   |\n",
+        "| **Precision**        | 1.000                   | 0.9439                    | 1.000                   |\n",
+        "| **Recall**           | 1.000                   | 0.9476                    | 1.000                   |\n",
+        "| **F1-Score**         | 1.000                   | 0.9458                    | 1.000                   |\n"
+      ],
+      "metadata": {
+        "id": "WvD5xr_wL4ZF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+        "\n",
+        "for name, model in models.items():\n",
+        "    y_pred = model.predict(x_tests[name])\n",
+        "    cm = confusion_matrix(y_test, y_pred)\n",
+        "\n",
+        "    fig, ax = plt.subplots(figsize=(3, ))\n",
+        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=label_encoders['class'].classes_)\n",
+        "    disp.plot(cmap='Blues',ax=ax)\n",
+        "    ax.set_title(f'Confusion Matrix: {name}')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 846
+        },
+        "id": "Aj0AQWt4Ksdz",
+        "outputId": "d8d2a3cb-df99-43ca-9339-4c22ebb1867b"
+      },
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 300x300 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Conclusion:\n",
+        "- K-Nearest Neighbors (KNN) and Random Forest both achieved perfect scores (1.0) across all metrics, with zero misclassifications in their confusion matrices. This means they correctly predicted all edible and poisonous mushrooms in the test set."
+      ],
+      "metadata": {
+        "id": "IVuWACmmMOhF"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "- **KNN** benefits from the clear separation in encoded feature space after scaling.\n",
+        "\n",
+        "- **Random Forest** handles categorical features very well and automatically selects important ones like odor, reducing overfitting risk.\n",
+        "\n",
+        "- **Logistic Regression** still performed very well (~94.8% accuracy), but it's a linear model, so it may struggle to capture complex feature interactions that tree-based models or KNN naturally handle better."
+      ],
+      "metadata": {
+        "id": "Eo-PnQ-zMcY8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "eUlK0HlGNCyi"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Sample Prediction"
+      ],
+      "metadata": {
+        "id": "zzj8ODpGNC2L"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Sample mushroom input\n",
+        "sample = {\n",
+        "    'cap-shape': 'x',\n",
+        "    'cap-surface': 's',\n",
+        "    'cap-color': 'n',\n",
+        "    'bruises': 't',\n",
+        "    'odor': 'p',\n",
+        "    'gill-attachment': 'f',\n",
+        "    'gill-spacing': 'c',\n",
+        "    'gill-size': 'n',\n",
+        "    'gill-color': 'k',\n",
+        "    'stalk-shape': 'e',\n",
+        "    'stalk-root': 'b',\n",
+        "    'stalk-surface-above-ring': 's',\n",
+        "    'stalk-surface-below-ring': 's',\n",
+        "    'stalk-color-above-ring': 'w',\n",
+        "    'stalk-color-below-ring': 'w',\n",
+        "    'veil-type': 'p',\n",
+        "    'veil-color': 'w',\n",
+        "    'ring-number': 'o',\n",
+        "    'ring-type': 'p',\n",
+        "    'spore-print-color': 'k',\n",
+        "    'population': 's',\n",
+        "    'habitat': 'u'\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "3Et7e7EVNEoC"
+      },
+      "execution_count": 60,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Encode the input using stored label encoders\n",
+        "\n",
+        "encoded_sample = []\n",
+        "for col in x.columns:\n",
+        "    encoded_val = label_encoders[col].transform([sample[col]])[0]\n",
+        "    encoded_sample.append(encoded_val)"
+      ],
+      "metadata": {
+        "id": "Zwp_FBiDNGIX"
+      },
+      "execution_count": 61,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Predict\n",
+        "sample_array = np.array(encoded_sample).reshape(1, -1)\n",
+        "sample_df = pd.DataFrame([encoded_sample], columns=x.columns)\n",
+        "sample_scaled = scaler.transform(sample_df)"
+      ],
+      "metadata": {
+        "id": "5t6aMW6NNL-P"
+      },
+      "execution_count": 65,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prediction = rf.predict(sample_scaled)  #\n",
+        "predicted_class = label_encoders['class'].inverse_transform(prediction)[0]\n",
+        "\n",
+        "print(\"Predicted Class:\", predicted_class)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iiQrVZJaNcqb",
+        "outputId": "7fa0f02c-c349-4c47-e355-cfaa3cf8b9d9"
+      },
+      "execution_count": 66,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Class: p\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prediction = logreg.predict(sample_scaled)  # Replace with rf, knn, or logreg\n",
+        "predicted_class = label_encoders['class'].inverse_transform(prediction)[0]\n",
+        "\n",
+        "print(\"Predicted Class:\", predicted_class)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qlQ3UAayNd7x",
+        "outputId": "66e292e9-c542-4a9d-a3e0-14bd745585b6"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Class: p\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/sklearn/utils/validation.py:2739: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prediction = knn.predict(sample_scaled)  # Replace with rf, knn, or logreg\n",
+        "predicted_class = label_encoders['class'].inverse_transform(prediction)[0]\n",
+        "\n",
+        "print(\"Predicted Class:\", predicted_class)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BwkoffaTNdm0",
+        "outputId": "37762022-a510-438a-e594-cfe05417b273"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted Class: p\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
diff --git a/Machine_Learning/Mushroom Classification/README.md b/Machine_Learning/Mushroom Classification/README.md
new file mode 100644
index 000000000..b13c2c962
--- /dev/null
+++ b/Machine_Learning/Mushroom Classification/README.md	
@@ -0,0 +1,89 @@
+# 🍄 Mushroom Classification 
+
+This project builds and compares multiple classification models to determine whether a mushroom is **edible** or **poisonous** based on its features. The dataset is processed and evaluated using **K-Nearest Neighbors (KNN)**, **Logistic Regression**, and **Random Forest** classifiers. Evaluation metrics such as accuracy, precision, recall, and F1-score are used to compare model performance.
+
+---
+
+## 📌 Objective  
+To classify mushrooms as **edible (e)** or **poisonous (p)** based on their categorical features. The goal is to build a robust model that generalizes well and can assist in safe mushroom identification.
+
+Comparing and evaluating three classification models:
+- K-Nearest Neighbors (KNN)
+- Logistic Regression
+- Random Forest
+
+… and determine which performs best for mushroom classification.
+
+---
+
+## 📂 Dataset  
+**Source:** [UCI Mushroom Dataset](https://www.kaggle.com/datasets/uciml/mushroom-classification) 
+
+**Target Variable:** `class` (e = edible, p = poisonous)
+
+**Features :**
+- cap-shape
+- cap-surface
+- cap-color
+- bruises
+- odor
+- gill-attachment
+- gill-spacing
+- gill-size
+- gill-color  
+- stalk-shape
+- stalk-root
+- stalk-surface-above-ring
+- stalk-surface-below-ring
+- stalk-color-above-ring
+- stalk-color-below-ring
+- veil-type	veil-color
+- ring-number
+- ring-type
+- spore-print-color
+- population
+- habitat
+
+---
+
+## 🔍 Exploratory Data Analysis (EDA)
+- Checked shape, data types, null values (none found).
+- Examined class distribution – fairly balanced.
+- Analyzed frequency of each feature's unique values.
+- Visualized correlation between selected categorical features and class.
+- Inspected label distribution with bar plots.
+
+---
+
+## ⚙️ Preprocessing  
+- Label encoding applied to all categorical variables.
+- Train-test split using `train_test_split` (test size = 20%).
+- Feature scaling using `StandardScaler` (for KNN & Logistic Regression).
+- All models trained on the same preprocessed data for fair comparison.
+
+---
+
+## 🧠 Model Comparison
+
+| Model               | Accuracy | Precision | Recall | F1-Score | interpretation |
+|--------------------|----------|-----------|--------|----------|-------|
+| KNN          | **100%**   | 1.00      | 1.00   | 1.00     | Great accuracy |
+| Logistic Regression | 94.77%   | 0.94      | 0.94   | 0.94     | Strong linear separator |
+| Random Forest       | **100%** | 1.00      | 1.00   | 1.00     | Perfect accuracy, robust model |
+
+---
+
+## 🏆 Final Verdict
+
+Both **KNN** and **Random Forest** achieved perfect classification on the test set. However:
+
+- **Random Forest** may generalize better to unseen data due to ensemble learning.
+- **KNN** is simpler but slower for large datasets at inference time.
+
+📌 In practical applications, **Random Forest** is preferred due to efficiency and robustness.
+
+---
+## 👤 Author
+
+GitHub: [archangel2006](https://github.com/archangel2006/)
+
diff --git a/Machine_Learning/Mushroom Classification/mushrooms.csv b/Machine_Learning/Mushroom Classification/mushrooms.csv
new file mode 100644
index 000000000..abf74eebe
--- /dev/null
+++ b/Machine_Learning/Mushroom Classification/mushrooms.csv	
@@ -0,0 +1,8125 @@
+class,cap-shape,cap-surface,cap-color,bruises,odor,gill-attachment,gill-spacing,gill-size,gill-color,stalk-shape,stalk-root,stalk-surface-above-ring,stalk-surface-below-ring,stalk-color-above-ring,stalk-color-below-ring,veil-type,veil-color,ring-number,ring-type,spore-print-color,population,habitat
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\ No newline at end of file