Mushrooms_Decision_Tree.ipynb

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              "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
              "0     p         x           s         n       t    p               f   \n",
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              "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",
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              "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",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "df = pd.read_csv('/content/mushrooms.csv')\n",
        "df.head()"
      ]
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        "df.shape"
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          "data": {
            "text/plain": [
              "(8124, 23)"
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          },
          "metadata": {},
          "execution_count": 60
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      "source": [
        "df.isnull().sum()"
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        "outputId": "98689a3b-ef5c-4427-c1a6-94aa50d15f38"
      },
      "execution_count": 61,
      "outputs": [
        {
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          "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"
            ]
          },
          "metadata": {},
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        }
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        "sns.countplot(df['class'])"
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        {
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            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6dad3aa4d0>"
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        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUiUlEQVR4nO3df5Bd9Xnf8ffH4lfS2AHMmhJJiYijNgEnkZ0tkDh/uDABQVuLZIwLtY1KmciZQmvPpKnB0wYbW51kYoca12aqFBnhOiHEjovK0BIVSFxnwo9VLAsEZtiCXaQRsEaATR3Tij79436VXotdncXsuXfFvl8zd/ac5/s95z53RqPPnB/3nlQVkiQdymvG3YAkafEzLCRJnQwLSVInw0KS1MmwkCR1OmLcDfThhBNOqFWrVo27DUk6rGzfvv2bVTUx29irMixWrVrF1NTUuNuQpMNKkm/MNeZpKElSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVKnV+U3uKVXu/959U+PuwUtQj/6m/f3tm+PLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSp97DIsmyJF9JcmtbPznJPUmmk/xhkqNa/ei2Pt3GVw3t48pWfzjJOX33LEn6XqM4sngf8NDQ+m8D11TVTwDPAJe2+qXAM61+TZtHklOAC4FTgbXAp5MsG0HfkqSm17BIsgL4e8B/aOsBzgQ+36ZsAc5vy+vaOm38rDZ/HXBTVb1QVY8B08BpffYtSfpefR9Z/FvgXwL/t62/Hni2qva39d3A8ra8HHgcoI0/1+b/dX2Wbf5akg1JppJMzczMLPTnkKQlrbewSPL3gaeqantf7zGsqjZV1WRVTU5MTIziLSVpyejzt6HeCrw9yXnAMcDrgE8AxyY5oh09rAD2tPl7gJXA7iRHAD8MPD1UP2B4G0nSCPR2ZFFVV1bViqpaxeAC9Z1V9S7gLuAdbdp64Ja2vLWt08bvrKpq9Qvb3VInA6uBe/vqW5L0UuP41dkPADcl+SjwFeD6Vr8e+GySaWAfg4ChqnYluRl4ENgPXFZVL/bd5M/9xo19v4UOQ9t/5+JxtyCNxUjCoqr+FPjTtvwos9zNVFXfBS6YY/uNwMb+OpQkHYrf4JYkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUqc+n8F9TJJ7k3w1ya4kH271G5I8lmRHe61p9SS5Nsl0kp1J3jK0r/VJHmmv9XO9pySpH30+/OgF4Myqej7JkcCXk/yXNvYbVfX5g+afy+CRqauB04HrgNOTHA9cBUwCBWxPsrWqnumxd0nSkD6fwV1V9XxbPbK96hCbrANubNvdDRyb5CTgHGBbVe1rAbENWNtX35Kkl+r1mkWSZUl2AE8x+A//nja0sZ1quibJ0a22HHh8aPPdrTZXXZI0Ir2GRVW9WFVrgBXAaUneBFwJ/CTwd4DjgQ8sxHsl2ZBkKsnUzMzMQuxSktSM5G6oqnoWuAtYW1V726mmF4DPAKe1aXuAlUObrWi1ueoHv8emqpqsqsmJiYk+PoYkLVl93g01keTYtvwDwC8BX2vXIUgS4HzggbbJVuDidlfUGcBzVbUXuB04O8lxSY4Dzm41SdKI9Hk31EnAliTLGITSzVV1a5I7k0wAAXYAv9bm3wacB0wD3wEuAaiqfUk+AtzX5l1dVft67FuSdJDewqKqdgJvnqV+5hzzC7hsjrHNwOYFbVCSNG9+g1uS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSpz4fq3pMknuTfDXJriQfbvWTk9yTZDrJHyY5qtWPbuvTbXzV0L6ubPWHk5zTV8+SpNn1eWTxAnBmVf0ssAZY256t/dvANVX1E8AzwKVt/qXAM61+TZtHklOAC4FTgbXAp9ujWiVJI9JbWNTA8231yPYq4Ezg862+BTi/La9r67Txs5Kk1W+qqheq6jEGz+g+ra++JUkv1es1iyTLkuwAngK2Af8DeLaq9rcpu4HlbXk58DhAG38OeP1wfZZtht9rQ5KpJFMzMzN9fBxJWrJ6DYuqerGq1gArGBwN/GSP77WpqiaranJiYqKvt5GkJWkkd0NV1bPAXcDPA8cmOaINrQD2tOU9wEqANv7DwNPD9Vm2kSSNQJ93Q00kObYt/wDwS8BDDELjHW3aeuCWtry1rdPG76yqavUL291SJwOrgXv76luS9FJHdE/5vp0EbGl3Lr0GuLmqbk3yIHBTko8CXwGub/OvBz6bZBrYx+AOKKpqV5KbgQeB/cBlVfVij31Lkg7SW1hU1U7gzbPUH2WWu5mq6rvABXPsayOwcaF7lCTNj9/gliR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktSpz8eqrkxyV5IHk+xK8r5W/1CSPUl2tNd5Q9tcmWQ6ycNJzhmqr2216SRX9NWzJGl2fT5WdT/w61X1l0leC2xPsq2NXVNVHxuenOQUBo9SPRX4EeC/JflbbfhTDJ7hvRu4L8nWqnqwx94lSUP6fKzqXmBvW/52koeA5YfYZB1wU1W9ADzWnsV94PGr0+1xrCS5qc01LCRpREZyzSLJKgbP476nlS5PsjPJ5iTHtdpy4PGhzXa32lz1g99jQ5KpJFMzMzML/AkkaWnrPSyS/BDwBeD9VfUt4DrgjcAaBkceH1+I96mqTVU1WVWTExMTC7FLSVLT5zULkhzJICg+V1V/DFBVTw6N/x5wa1vdA6wc2nxFq3GIuiRpBOZ1ZJHkjvnUDhoPcD3wUFX97lD9pKFpvww80Ja3AhcmOTrJycBq4F7gPmB1kpOTHMXgIvjW+fQtSVoYhzyySHIM8IPACe3aQtrQ6zj0xWqAtwLvAe5PsqPVPghclGQNUMDXgfcCVNWuJDczuHC9H7isql5sfVwO3A4sAzZX1a6X8yElSa9M12mo9wLvZ3Ar63b+f1h8C/h3h9qwqr48NH/YbYfYZiOwcZb6bYfaTpLUr0OGRVV9AvhEkn9WVZ8cUU+SpEVmXhe4q+qTSX4BWDW8TVXd2FNfkqRFZF5hkeSzDG533QG82MoFGBaStATM99bZSeCUqqo+m5EkLU7z/VLeA8Df7LMRSdLiNd8jixOAB5PcC7xwoFhVb++lK0nSojLfsPhQn01Ikha3+d4N9Wd9NyJJWrzmezfUtxnc/QRwFHAk8L+q6nV9NSZJWjzme2Tx2gPL7Tef1gFn9NWUJGlxedk/UV4D/wk4p3OyJOlVYb6noX5laPU1DL538d1eOpIkLTrzvRvqHwwt72fwa7HrFrwbSdKiNN9rFpf03YgkafGa78OPViT5YpKn2usLSVb03ZwkaXGY7wXuzzB4Ot2PtNd/brU5JVmZ5K4kDybZleR9rX58km1JHml/j2v1JLk2yXSSnUneMrSv9W3+I0nWfz8fVJL0/ZtvWExU1Weqan973QBMdGyzH/j1qjqFwW22lyU5BbgCuKOqVgN3tHWAcxk8SnU1sAG4DgbhAlwFnA6cBlx1IGAkSaMx37B4Osm7kyxrr3cDTx9qg6raW1V/2Za/DTzE4FGs64AtbdoW4Py2vA64sd2aezdwbHte9znAtqraV1XPANuAtS/jM0qSXqH5hsU/Ad4JPAHsBd4B/OP5vkmSVcCbgXuAE6tqbxt6AjixLS8HHh/abHerzVU/+D02JJlKMjUzMzPf1iRJ8zDfsLgaWF9VE1X1Bgbh8eH5bJjkh4AvAO+vqm8Nj7XnYyzIMzKqalNVTVbV5MRE1xkySdLLMd+w+Jl2CgiAqtrH4EjhkJIcySAoPldVf9zKT7bTS7S/T7X6HmDl0OYrWm2uuiRpROYbFq8ZvqjcLjof8jsa7TekrgceqqrfHRraChy4o2k9cMtQ/eJ2V9QZwHPtdNXtwNlJjms9nN1qkqQRme83uD8O/EWSP2rrFwAbO7Z5K/Ae4P4kO1rtg8BvATcnuRT4BoNrIQC3AecB08B3gEtgcBST5CPAfW3e1e3IRpI0IvP9BveNSaaAM1vpV6rqwY5tvgxkjuGzZplfwGVz7GszsHk+vUqSFt58jyxo4XDIgJAkvTq97J8olyQtPYaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6tRbWCTZnOSpJA8M1T6UZE+SHe113tDYlUmmkzyc5Jyh+tpWm05yRV/9SpLm1ueRxQ3A2lnq11TVmva6DSDJKcCFwKltm08nWZZkGfAp4FzgFOCiNleSNELzflLey1VVX0qyap7T1wE3VdULwGNJpoHT2th0VT0KkOSmNtcn9knSCI3jmsXlSXa201THtdpy4PGhObtbba76SyTZkGQqydTMzEwffUvSkjXqsLgOeCOwBtgLfHyhdlxVm6pqsqomJyYmFmq3kiR6PA01m6p68sBykt8Dbm2re4CVQ1NXtBqHqEuSRmSkRxZJThpa/WXgwJ1SW4ELkxyd5GRgNXAvcB+wOsnJSY5icBF86yh7liT1eGSR5A+AtwEnJNkNXAW8LckaoICvA+8FqKpdSW5mcOF6P3BZVb3Y9nM5cDuwDNhcVbv66lmSNLs+74a6aJby9YeYvxHYOEv9NuC2BWxNkvQy+Q1uSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR16i0skmxO8lSSB4ZqxyfZluSR9ve4Vk+Sa5NMJ9mZ5C1D26xv8x9Jsr6vfiVJc+vzyOIGYO1BtSuAO6pqNXBHWwc4l8GjVFcDG4DrYBAuDJ6wdzpwGnDVgYCRJI1Ob2FRVV8C9h1UXgdsactbgPOH6jfWwN3Ase153ecA26pqX1U9A2zjpQEkSerZqK9ZnFhVe9vyE8CJbXk58PjQvN2tNlddkjRCY7vAXVUF1ELtL8mGJFNJpmZmZhZqt5IkRh8WT7bTS7S/T7X6HmDl0LwVrTZX/SWqalNVTVbV5MTExII3LklL2ajDYitw4I6m9cAtQ/WL211RZwDPtdNVtwNnJzmuXdg+u9UkSSN0RF87TvIHwNuAE5LsZnBX028BNye5FPgG8M42/TbgPGAa+A5wCUBV7UvyEeC+Nu/qqjr4orkkqWe9hUVVXTTH0FmzzC3gsjn2sxnYvICtSZJeJr/BLUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKnTWMIiydeT3J9kR5KpVjs+ybYkj7S/x7V6klybZDrJziRvGUfPkrSUjfPI4u9W1ZqqmmzrVwB3VNVq4I62DnAusLq9NgDXjbxTSVriFtNpqHXAlra8BTh/qH5jDdwNHJvkpHE0KElL1bjCooA/SbI9yYZWO7Gq9rblJ4AT2/Jy4PGhbXe32vdIsiHJVJKpmZmZvvqWpCXpiDG97y9W1Z4kbwC2Jfna8GBVVZJ6OTusqk3AJoDJycmXta0k6dDGcmRRVXva36eALwKnAU8eOL3U/j7Vpu8BVg5tvqLVJEkjMvKwSPI3krz2wDJwNvAAsBVY36atB25py1uBi9tdUWcAzw2drpIkjcA4TkOdCHwxyYH3//2q+q9J7gNuTnIp8A3gnW3+bcB5wDTwHeCS0bcsSUvbyMOiqh4FfnaW+tPAWbPUC7hsBK1JkuawmG6dlSQtUoaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6HTZhkWRtkoeTTCe5Ytz9SNJScliERZJlwKeAc4FTgIuSnDLeriRp6TgswgI4DZiuqker6n8DNwHrxtyTJC0ZI38G9/dpOfD40Ppu4PThCUk2ABva6vNJHh5Rb0vBCcA3x93EYpCPrR93C3op/30ecFVe6R5+bK6BwyUsOlXVJmDTuPt4NUoyVVWT4+5Dmo3/PkfjcDkNtQdYObS+otUkSSNwuITFfcDqJCcnOQq4ENg65p4kack4LE5DVdX+JJcDtwPLgM1VtWvMbS0lnt7TYua/zxFIVY27B0nSIne4nIaSJI2RYSFJ6mRYSJI6GRaSpE6GheaUZFWSryX5XJKHknw+yQ+Ouy8JIMm7k9ybZEeSf99+Q049MSzU5W8Dn66qnwK+BfzTMfcjkeSngH8IvLWq1gAvAu8ab1evboaFujxeVX/elv8j8IvjbEZqzgJ+DrgvyY62/uPjbenV7bD4Up7G6uAv4vjFHC0GAbZU1ZXjbmSp8MhCXX40yc+35X8EfHmczUjNHcA7krwBIMnxSeb8xVS9coaFujwMXJbkIeA44Lox9yNRVQ8C/wr4kyQ7gW3ASePt6tXNn/vQnJKsAm6tqjeNuRVJY+aRhSSpk0cWkqROHllIkjoZFpKkToaFJKmTYSH1IMmHkvyLcfchLRTDQpLUybCQFkCSi5PsTPLVJJ89aOxXk9zXxr5w4Jd7k1yQ5IFW/1KrnTr0S6o7k6wex+eRDuats9IrlORU4IvAL1TVN5McD/xz4Pmq+liS11fV023uR4Enq+qTSe4H1lbVniTHVtWzST4J3F1Vn0tyFLCsqv5qXJ9NOsAjC+mVOxP4o6r6JkBV7Tto/E1J/nsLh3cBp7b6nwM3JPlV4MCzGP4C+GCSDwA/ZlBosTAspP7dAFxeVT8NfBg4BqCqfo3B7xutBLa3I5DfB94O/BVwW5Izx9Oy9L0MC+mVuxO4IMnrYfALqAeNvxbYm+RIhh7Qk+SNVXVPVf0mMAOsTPLjwKNVdS1wC/AzI/kEUgefZyG9QlW1K8lG4M+SvAh8Bfj60JR/DdzDIBDuYRAeAL/TLmCHwU9ufxX4APCeJP8HeAL4NyP5EFIHL3BLkjp5GkqS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmd/h+fz8AyyrfJIgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.ma.core import shape\n",
        "plt.figure(figsize = (14, 6))\n",
        "g = sns.catplot(x = 'cap-shape', hue = 'cap-color', col = 'class', data = df, kind = 'count',\n",
        "                height = 15, aspect = 1);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "id": "Cm3yzetWsyfd",
        "outputId": "dc786282-1054-4e93-c3ca-7811a70cdbd0"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2214x1080 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (14, 6))\n",
        "g = sns.catplot(x = 'odor', hue = 'bruises', col = 'class', data = df, kind = 'count',\n",
        "                height = 18, aspect = 1, palette = 'inferno');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "RHJNCGFItvsE",
        "outputId": "fed032f7-34f8-4a45-8091-21d4fcd691a4"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2636.25x1296 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "df = df.apply(LabelEncoder().fit_transform)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "Vf4XWSyPukNV",
        "outputId": "c1a6e032-fdc2-483e-f100-5de777e08d69"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   class  cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
              "0      1          5            2          4        1     6                1   \n",
              "1      0          5            2          9        1     0                1   \n",
              "2      0          0            2          8        1     3                1   \n",
              "3      1          5            3          8        1     6                1   \n",
              "4      0          5            2          3        0     5                1   \n",
              "\n",
              "   gill-spacing  gill-size  gill-color  ...  stalk-surface-below-ring  \\\n",
              "0             0          1           4  ...                         2   \n",
              "1             0          0           4  ...                         2   \n",
              "2             0          0           5  ...                         2   \n",
              "3             0          1           5  ...                         2   \n",
              "4             1          0           4  ...                         2   \n",
              "\n",
              "   stalk-color-above-ring  stalk-color-below-ring  veil-type  veil-color  \\\n",
              "0                       7                       7          0           2   \n",
              "1                       7                       7          0           2   \n",
              "2                       7                       7          0           2   \n",
              "3                       7                       7          0           2   \n",
              "4                       7                       7          0           2   \n",
              "\n",
              "   ring-number  ring-type  spore-print-color  population  habitat  \n",
              "0            1          4                  2           3        5  \n",
              "1            1          4                  3           2        1  \n",
              "2            1          4                  3           2        3  \n",
              "3            1          4                  2           3        5  \n",
              "4            1          0                  3           0        1  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0baafc7f-6681-43ac-86b2-cece1c7ae4e0')\"\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0baafc7f-6681-43ac-86b2-cece1c7ae4e0 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-0baafc7f-6681-43ac-86b2-cece1c7ae4e0');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.drop(['class'], axis = 1)\n",
        "y = df['class']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)"
      ],
      "metadata": {
        "id": "rGBDA9Lpv1k7"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "df = DecisionTreeClassifier(random_state = 0, max_depth = 5)\n",
        "df.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "li-Vb7TZv9Ql",
        "outputId": "e70b4ec2-14ca-497d-9a6e-7691a8089ee0"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5, random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMXhHqJUw7f4",
        "outputId": "1e8e54e1-46ca-4197-fd79-5fd2dc9b65df"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9778403095321843"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = df.predict(x_test)"
      ],
      "metadata": {
        "id": "Ro9cLI9lxEA-"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test, predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXmQwTlDxNVf",
        "outputId": "abd1edc0-eeff-4b3d-d6cb-ad78695153f3"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9827727645611156"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier(max_depth = 5)\n",
        "\n",
        "rf.fit(x_train, y_train)\n",
        "rf.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLz8MOM1xZg6",
        "outputId": "3bc18eb6-1948-4537-c46c-1dffc17a624b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9910306014773127"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = rf.predict(x_test)\n",
        "rf.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFRh5udbxzQO",
        "outputId": "69a87a38-e128-4318-ebb6-e8eb7c757812"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9913863822805579"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtwmiAidx-hn",
        "outputId": "247f90c8-71e6-4d87-d02a-b605357e1e26"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9913863822805579"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    }
  ]
}