Decision_tree_.ipynb

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      "source": [
        "#import library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read the dataset\n",
        "df= pd.read_csv('/content/gameData.csv')"
      ],
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "#check the five data\n",
        "df.head()"
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              "   Days Outlook Temprature  Routine Wear Jacket?\n",
              "0     1   Sunny       Cold   Indoor           No\n",
              "1     2  Cloudy       Cold   Indoor          Yes\n",
              "2     3  Cloudy       Warm  Outdoor           No\n",
              "3     4   Sunny       Cold  Outdoor          Yes\n",
              "4     5  Cloudy       Cold  Outdoor          Yes"
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      "source": [
        "#copy the data \n",
        "df2 =df.copy()\n",
        "df3 =df.copy()"
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      "source": [
        "#drop Days column\n",
        "df.drop(\"Days\",axis=\"columns\", inplace =True)"
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      "execution_count": 5,
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            "text/plain": [
              "  Outlook Temprature  Routine Wear Jacket?\n",
              "0   Sunny       Cold   Indoor           No\n",
              "1  Cloudy       Cold   Indoor          Yes\n",
              "2  Cloudy       Warm  Outdoor           No\n",
              "3   Sunny       Cold  Outdoor          Yes\n",
              "4  Cloudy       Cold  Outdoor          Yes"
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              "      <td>Cloudy</td>\n",
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      "source": [
        "x=df.drop('Wear Jacket?',axis=1)"
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      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#extract the wear jacket column\n",
        "y= df[['Wear Jacket?']]"
      ],
      "metadata": {
        "id": "KI7hRilYxtAa"
      },
      "execution_count": 8,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "#take five rows\n",
        "y.head()"
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        "id": "m06ASoGWxvGw",
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          "data": {
            "text/plain": [
              "  Wear Jacket?\n",
              "0           No\n",
              "1          Yes\n",
              "2           No\n",
              "3          Yes\n",
              "4          Yes"
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              "      <td>Yes</td>\n",
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              "      <td>Yes</td>\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b0fa21cb-aeda-4c91-ab8a-51896f9cca21 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-b0fa21cb-aeda-4c91-ab8a-51896f9cca21');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Wear Jacket?', axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "g87JK-tFxvIT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check the header\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "rpzhXAhyxvMI",
        "outputId": "5cc783e6-1b38-40fa-9d2b-77b20b82e803"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Outlook Temprature  Routine\n",
              "0   Sunny       Cold   Indoor\n",
              "1  Cloudy       Cold   Indoor\n",
              "2  Cloudy       Warm  Outdoor\n",
              "3   Sunny       Cold  Outdoor\n",
              "4  Cloudy       Cold  Outdoor\n",
              "5   Sunny       Warm  Outdoor\n",
              "6  Cloudy       Warm   Indoor\n",
              "7   Sunny       Warm   Indoor"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-505fe05b-98a3-4152-a68c-89c02002481e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        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>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Cold</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Outdoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Cloudy</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sunny</td>\n",
              "      <td>Warm</td>\n",
              "      <td>Indoor</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-505fe05b-98a3-4152-a68c-89c02002481e')\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-505fe05b-98a3-4152-a68c-89c02002481e');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "from pandas.core.dtypes.common import is_numeric_dtype"
      ],
      "metadata": {
        "id": "d2WkQBGC_XLr"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use Label encoder to encode dataset\n",
        "le = LabelEncoder()\n",
        "le.fit_transform([])"
      ],
      "metadata": {
        "id": "q8GMeC21xvO8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db1497e4-48c0-4126-b253-6aef0bf36b24"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loop\n",
        "for col in x.columns:\n",
        "  if is_numeric_dtype(x[col]):\n",
        "    continue\n",
        "  x[col] =le.fit_transform(x[col])"
      ],
      "metadata": {
        "id": "94sGAnTjzSBR"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#show the encode value\n",
        "x.head()"
      ],
      "metadata": {
        "id": "SK0lB3sKzR8L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0cb181c7-d25f-41e2-8f5d-7a38114f19ab"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Outlook  Temprature  Routine\n",
              "0        1           0        0\n",
              "1        0           0        0\n",
              "2        0           1        1\n",
              "3        1           0        1\n",
              "4        0           0        1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37695acb-d934-41fa-b729-96d2a2c3b6d2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Outlook</th>\n",
              "      <th>Temprature</th>\n",
              "      <th>Routine</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37695acb-d934-41fa-b729-96d2a2c3b6d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37695acb-d934-41fa-b729-96d2a2c3b6d2 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-37695acb-d934-41fa-b729-96d2a2c3b6d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sunny --> 1\n",
        "\n",
        "cloudy--> 0\n",
        "\n",
        "cold  --> 0\n",
        "\n",
        "warm  --> 1\n",
        "\n",
        "indoor--> 0\n",
        "\n",
        "outdoor-->1"
      ],
      "metadata": {
        "id": "i_MNa9fACebf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "LG21p0KhCWv0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create an object of decisiontreeClassifier\n",
        "clf =DecisionTreeClassifier(random_state=0)"
      ],
      "metadata": {
        "id": "iDOJKtS2CYsw"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#fit the data\n",
        "clf.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNYrESjGh4uF",
        "outputId": "6d287d24-d1f5-47f1-a7e3-79a3e95cbf9c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predict the data\n",
        "clf.predict([[1,0,0]])"
      ],
      "metadata": {
        "id": "rl48bLHWCY30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13f6d109-eb36-4577-f002-a86021464d91"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['No'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict([[0,0,1]])"
      ],
      "metadata": {
        "id": "VuXYZWDJCY5L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d9cb89b-08d3-4a1a-d5f8-cad964f0c541"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import tree library\n",
        "from sklearn import tree\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "U_WyotEXCY9O"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ploting data\n",
        "tree.plot_tree(clf)"
      ],
      "metadata": {
        "id": "mrmYeA-ZCZAI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "47169b67-c522-47af-bad1-394bbf27ca09"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6666666666666666, 0.875, 'X[1] <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.5, 0.625, 'X[2] <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.3333333333333333, 0.375, 'X[0] <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ploting data with feature name\n",
        "plt.figure(figsize=(10,10))\n",
        "tree.plot_tree(clf, feature_names=x.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "bhIW-LS0lcm7",
        "outputId": "c966321f-62ca-48c6-8d0b-49b15ffbbfb1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6666666666666666, 0.875, 'Temprature <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.5, 0.625, 'Routine <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.3333333333333333, 0.375, 'Outlook <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#show the updated ploting\n",
        "plt.figure(figsize=(10,10))\n",
        "tree.plot_tree(clf, feature_names=x.columns, rounded =True, filled =True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "CeyetzlSkBVk",
        "outputId": "89abde88-544c-4ebe-cc80-2a7e284dc8b3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.6666666666666666, 0.875, 'Temprature <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
              " Text(0.5, 0.625, 'Routine <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
              " Text(0.3333333333333333, 0.375, 'Outlook <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
              " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
              " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
              " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
              " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}