Decision tree_Mushroom_edibility.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Mushroom edibility",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqKnYlt3O9gs"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13udN3mnPVYE"
      },
      "source": [
        "df=pd.read_csv('https://raw.githubusercontent.com/shivendutyagi/Datasets/main/mushrooms.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "75tRGIyPPYe3",
        "outputId": "d1422639-2c75-4e68-b843-6cfc15cbd5ac"
      },
      "source": [
        "df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>stalk-shape</th>\n",
              "      <th>stalk-root</th>\n",
              "      <th>stalk-surface-above-ring</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",
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              "      <th>2</th>\n",
              "      <td>e</td>\n",
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              "      <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>e</td>\n",
              "      <td>c</td>\n",
              "      <td>s</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",
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              "      <td>w</td>\n",
              "      <td>t</td>\n",
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              "      <th>8119</th>\n",
              "      <td>e</td>\n",
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              "      <th>8120</th>\n",
              "      <td>e</td>\n",
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              "      <td>?</td>\n",
              "      <td>s</td>\n",
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              "      <td>o</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>n</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>8122</th>\n",
              "      <td>p</td>\n",
              "      <td>k</td>\n",
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              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>y</td>\n",
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              "      <td>c</td>\n",
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              "      <td>s</td>\n",
              "      <td>k</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>w</td>\n",
              "      <td>v</td>\n",
              "      <td>l</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8123</th>\n",
              "      <td>e</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>n</td>\n",
              "      <td>a</td>\n",
              "      <td>c</td>\n",
              "      <td>b</td>\n",
              "      <td>y</td>\n",
              "      <td>e</td>\n",
              "      <td>?</td>\n",
              "      <td>s</td>\n",
              "      <td>s</td>\n",
              "      <td>o</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>o</td>\n",
              "      <td>o</td>\n",
              "      <td>p</td>\n",
              "      <td>o</td>\n",
              "      <td>c</td>\n",
              "      <td>l</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8124 rows × 23 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     class cap-shape cap-surface  ... spore-print-color population habitat\n",
              "0        p         x           s  ...                 k          s       u\n",
              "1        e         x           s  ...                 n          n       g\n",
              "2        e         b           s  ...                 n          n       m\n",
              "3        p         x           y  ...                 k          s       u\n",
              "4        e         x           s  ...                 n          a       g\n",
              "...    ...       ...         ...  ...               ...        ...     ...\n",
              "8119     e         k           s  ...                 b          c       l\n",
              "8120     e         x           s  ...                 b          v       l\n",
              "8121     e         f           s  ...                 b          c       l\n",
              "8122     p         k           y  ...                 w          v       l\n",
              "8123     e         x           s  ...                 o          c       l\n",
              "\n",
              "[8124 rows x 23 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujR17a28P3S1",
        "outputId": "d86f779d-5add-457b-ced4-6467e393ccd3"
      },
      "source": [
        "for column in df.columns:\n",
        "  print('Unique values in',column+':',\n",
        "        df[column].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in class: ['p' 'e']\n",
            "Unique values in cap-shape: ['x' 'b' 's' 'f' 'k' 'c']\n",
            "Unique values in cap-surface: ['s' 'y' 'f' 'g']\n",
            "Unique values in cap-color: ['n' 'y' 'w' 'g' 'e' 'p' 'b' 'u' 'c' 'r']\n",
            "Unique values in bruises: ['t' 'f']\n",
            "Unique values in odor: ['p' 'a' 'l' 'n' 'f' 'c' 'y' 's' 'm']\n",
            "Unique values in gill-attachment: ['f' 'a']\n",
            "Unique values in gill-spacing: ['c' 'w']\n",
            "Unique values in gill-size: ['n' 'b']\n",
            "Unique values in gill-color: ['k' 'n' 'g' 'p' 'w' 'h' 'u' 'e' 'b' 'r' 'y' 'o']\n",
            "Unique values in stalk-shape: ['e' 't']\n",
            "Unique values in stalk-root: ['e' 'c' 'b' 'r' '?']\n",
            "Unique values in stalk-surface-above-ring: ['s' 'f' 'k' 'y']\n",
            "Unique values in stalk-surface-below-ring: ['s' 'f' 'y' 'k']\n",
            "Unique values in stalk-color-above-ring: ['w' 'g' 'p' 'n' 'b' 'e' 'o' 'c' 'y']\n",
            "Unique values in stalk-color-below-ring: ['w' 'p' 'g' 'b' 'n' 'e' 'y' 'o' 'c']\n",
            "Unique values in veil-type: ['p']\n",
            "Unique values in veil-color: ['w' 'n' 'o' 'y']\n",
            "Unique values in ring-number: ['o' 't' 'n']\n",
            "Unique values in ring-type: ['p' 'e' 'l' 'f' 'n']\n",
            "Unique values in spore-print-color: ['k' 'n' 'u' 'h' 'w' 'r' 'o' 'y' 'b']\n",
            "Unique values in population: ['s' 'n' 'a' 'v' 'y' 'c']\n",
            "Unique values in habitat: ['u' 'g' 'm' 'd' 'p' 'w' 'l']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SC7b-yl1RuEh"
      },
      "source": [
        "y=df['class']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bryoWPtRR-io"
      },
      "source": [
        "y[y=='p']=1\n",
        "y[y=='e']=0\n",
        "y=y.astype(np.uint8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pv4v2vRHSNa-"
      },
      "source": [
        "X=df.drop(['class'],axis=1)\n",
        "X_encoded=pd.get_dummies(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UYuivo_ZSWiF"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "Xtrain,Xtest,ytrain,ytest=train_test_split(X_encoded,y,test_size=0.3,random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CP7lmaB9Soyr"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier=DecisionTreeClassifier()\n",
        "dt=classifier.fit(Xtrain,ytrain)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-LSc7BmS-YO"
      },
      "source": [
        "from sklearn.tree import plot_tree\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "7JH99tVBUCFA",
        "outputId": "517e252b-297b-4b12-d7f3-c9e0ba82bd2e"
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plot_tree(dt,filled=True,class_names=['Edible','Poisonous'],feature_names=X_encoded.columns);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(525.1764705882352, 407.7, 'odor_n <= 0.5\\ngini = 0.499\\nsamples = 5686\\nvalue = [2951, 2735]\\nclass = Edible'),\n",
              " Text(315.1058823529412, 353.34000000000003, 'stalk-root_c <= 0.5\\ngini = 0.28\\nsamples = 3191\\nvalue = [537, 2654]\\nclass = Poisonous'),\n",
              " Text(210.0705882352941, 298.98, 'stalk-root_r <= 0.5\\ngini = 0.122\\nsamples = 2809\\nvalue = [184, 2625]\\nclass = Poisonous'),\n",
              " Text(157.5529411764706, 244.62, 'odor_a <= 0.5\\ngini = 0.044\\nsamples = 2685\\nvalue = [60, 2625]\\nclass = Poisonous'),\n",
              " Text(105.03529411764706, 190.26, 'odor_l <= 0.5\\ngini = 0.022\\nsamples = 2654\\nvalue = [29, 2625]\\nclass = Poisonous'),\n",
              " Text(52.51764705882353, 135.89999999999998, 'gini = 0.0\\nsamples = 2625\\nvalue = [0, 2625]\\nclass = Poisonous'),\n",
              " Text(157.5529411764706, 135.89999999999998, 'gini = 0.0\\nsamples = 29\\nvalue = [29, 0]\\nclass = Edible'),\n",
              " Text(210.0705882352941, 190.26, 'gini = 0.0\\nsamples = 31\\nvalue = [31, 0]\\nclass = Edible'),\n",
              " Text(262.5882352941176, 244.62, 'gini = 0.0\\nsamples = 124\\nvalue = [124, 0]\\nclass = Edible'),\n",
              " Text(420.1411764705882, 298.98, 'bruises_f <= 0.5\\ngini = 0.14\\nsamples = 382\\nvalue = [353, 29]\\nclass = Edible'),\n",
              " Text(367.6235294117647, 244.62, 'gini = 0.0\\nsamples = 353\\nvalue = [353, 0]\\nclass = Edible'),\n",
              " Text(472.65882352941173, 244.62, 'gini = 0.0\\nsamples = 29\\nvalue = [0, 29]\\nclass = Poisonous'),\n",
              " Text(735.2470588235294, 353.34000000000003, 'spore-print-color_r <= 0.5\\ngini = 0.063\\nsamples = 2495\\nvalue = [2414, 81]\\nclass = Edible'),\n",
              " Text(682.7294117647059, 298.98, 'stalk-surface-below-ring_y <= 0.5\\ngini = 0.026\\nsamples = 2446\\nvalue = [2414, 32]\\nclass = Edible'),\n",
              " Text(577.6941176470588, 244.62, 'cap-shape_c <= 0.5\\ngini = 0.004\\nsamples = 2408\\nvalue = [2403, 5]\\nclass = Edible'),\n",
              " Text(525.1764705882352, 190.26, 'cap-surface_g <= 0.5\\ngini = 0.002\\nsamples = 2406\\nvalue = [2403, 3]\\nclass = Edible'),\n",
              " Text(472.65882352941173, 135.89999999999998, 'gill-size_b <= 0.5\\ngini = 0.002\\nsamples = 2405\\nvalue = [2403, 2]\\nclass = Edible'),\n",
              " Text(420.1411764705882, 81.54000000000002, 'population_c <= 0.5\\ngini = 0.029\\nsamples = 135\\nvalue = [133, 2]\\nclass = Edible'),\n",
              " Text(367.6235294117647, 27.180000000000007, 'gini = 0.0\\nsamples = 133\\nvalue = [133, 0]\\nclass = Edible'),\n",
              " Text(472.65882352941173, 27.180000000000007, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]\\nclass = Poisonous'),\n",
              " Text(525.1764705882352, 81.54000000000002, 'gini = 0.0\\nsamples = 2270\\nvalue = [2270, 0]\\nclass = Edible'),\n",
              " Text(577.6941176470588, 135.89999999999998, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]\\nclass = Poisonous'),\n",
              " Text(630.2117647058824, 190.26, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]\\nclass = Poisonous'),\n",
              " Text(787.7647058823529, 244.62, 'ring-type_e <= 0.5\\ngini = 0.411\\nsamples = 38\\nvalue = [11, 27]\\nclass = Poisonous'),\n",
              " Text(735.2470588235294, 190.26, 'gini = 0.0\\nsamples = 11\\nvalue = [11, 0]\\nclass = Edible'),\n",
              " Text(840.2823529411764, 190.26, 'gini = 0.0\\nsamples = 27\\nvalue = [0, 27]\\nclass = Poisonous'),\n",
              " Text(787.7647058823529, 298.98, 'gini = 0.0\\nsamples = 49\\nvalue = [0, 49]\\nclass = Poisonous')]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvw1TJrAUc1V",
        "outputId": "c47e0e46-9a09-4ee8-98ef-1ee26c835166"
      },
      "source": [
        "score=cross_val_score(dt,Xtrain,ytrain,cv=5)\n",
        "for scores in score:\n",
        "  print('Accuracy:',round(scores,4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Accuracy: 1.0\n",
            "Accuracy: 1.0\n",
            "Accuracy: 1.0\n",
            "Accuracy: 0.9982\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "chFONHCjVYSt",
        "outputId": "be00d36c-5a7d-4f5f-857f-de8a270d4345"
      },
      "source": [
        "plot_confusion_matrix(dt,Xtest,ytest,display_labels=['Edible','Poisonous']);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuc1NNg3V068"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}