CO544_Decision_Trees.ipynb

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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NXX7VXFbW_a3"
      },
      "outputs": [],
      "source": [
        " ! pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "EXJWGdNTXGkf",
        "outputId": "64ee1a17-032a-447d-f353-72e6947e062f"
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      "execution_count": 2,
      "outputs": [
        {
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              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "  return element;\n",
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              "\n",
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              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
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              "\n",
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              "  cancel.textContent = 'Cancel upload';\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
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              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle (1).json to kaggle (1).json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle (1).json': b'{\"username\":\"nishanikasineshan\",\"key\":\"4425a83486cdf10e8e202235a42d5e25\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "id": "joTIOJmeXMLs"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "2uDS8BoMXqCz"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "f_-fBDlMXvzn"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d uciml/pima-indians-diabetes-database"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q23argYmXyRI",
        "outputId": "5a9b875d-44a0-40da-a85c-4af708ea83fb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading pima-indians-diabetes-database.zip to /content\n",
            "\r  0% 0.00/8.91k [00:00<?, ?B/s]\n",
            "\r100% 8.91k/8.91k [00:00<00:00, 25.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip pima-indians-diabetes-database.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUw2rQVzX95g",
        "outputId": "ad91e020-88af-4fb0-8280-bff35a265f16"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  pima-indians-diabetes-database.zip\n",
            "  inflating: diabetes.csv            \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import required libraries\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier # Decision tree classifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn import metrics # scikit-learn metrics module for computing accuracy\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore') # Ignore warning messages"
      ],
      "metadata": {
        "id": "Iow_yjREYDzd"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#loading dataset\n",
        "diabetes_df = pd.read_csv('diabetes.csv')\n",
        "diabetes_df.head() # Preview the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Auf_RZNTYQNt",
        "outputId": "53739710-a8f3-4593-cf5d-b30bbd9e4534"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ],
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.shape # Number of instances and variables"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGTW3d1HYaD9",
        "outputId": "3e3c1062-113d-445d-fe6f-7dbfbfbd6456"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#rename columns\n",
        "col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age','label'] # Define new column names\n",
        "diabetes_df.columns = col_names # Rename column names"
      ],
      "metadata": {
        "id": "Oni7IG9IYbWY"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0XQC-a40YuUr",
        "outputId": "83433d96-f267-4e2e-d168-95ffff245e73"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree',\n",
              "       'age', 'label'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dataset summary\n",
        "diabetes_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "leppkH0PY1KD",
        "outputId": "111d99a2-068e-471d-d3ed-2540ec88e6d5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   pregnant  768 non-null    int64  \n",
            " 1   glucose   768 non-null    int64  \n",
            " 2   bp        768 non-null    int64  \n",
            " 3   skin      768 non-null    int64  \n",
            " 4   insulin   768 non-null    int64  \n",
            " 5   bmi       768 non-null    float64\n",
            " 6   pedigree  768 non-null    float64\n",
            " 7   age       768 non-null    int64  \n",
            " 8   label     768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#freq distributions of values in variables\n",
        "for col in col_names:\n",
        "  print(diabetes_df[col].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1TePhteaY7pE",
        "outputId": "a3cda177-157b-40b6-a484-4c5ef9e0f819"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1     135\n",
            "0     111\n",
            "2     103\n",
            "3      75\n",
            "4      68\n",
            "5      57\n",
            "6      50\n",
            "7      45\n",
            "8      38\n",
            "9      28\n",
            "10     24\n",
            "11     11\n",
            "13     10\n",
            "12      9\n",
            "14      2\n",
            "15      1\n",
            "17      1\n",
            "Name: pregnant, dtype: int64\n",
            "99     17\n",
            "100    17\n",
            "111    14\n",
            "129    14\n",
            "125    14\n",
            "       ..\n",
            "191     1\n",
            "177     1\n",
            "44      1\n",
            "62      1\n",
            "190     1\n",
            "Name: glucose, Length: 136, dtype: int64\n",
            "70     57\n",
            "74     52\n",
            "78     45\n",
            "68     45\n",
            "72     44\n",
            "64     43\n",
            "80     40\n",
            "76     39\n",
            "60     37\n",
            "0      35\n",
            "62     34\n",
            "66     30\n",
            "82     30\n",
            "88     25\n",
            "84     23\n",
            "90     22\n",
            "86     21\n",
            "58     21\n",
            "50     13\n",
            "56     12\n",
            "52     11\n",
            "54     11\n",
            "75      8\n",
            "92      8\n",
            "65      7\n",
            "85      6\n",
            "94      6\n",
            "48      5\n",
            "96      4\n",
            "44      4\n",
            "100     3\n",
            "106     3\n",
            "98      3\n",
            "110     3\n",
            "55      2\n",
            "108     2\n",
            "104     2\n",
            "46      2\n",
            "30      2\n",
            "122     1\n",
            "95      1\n",
            "102     1\n",
            "61      1\n",
            "24      1\n",
            "38      1\n",
            "40      1\n",
            "114     1\n",
            "Name: bp, dtype: int64\n",
            "0     227\n",
            "32     31\n",
            "30     27\n",
            "27     23\n",
            "23     22\n",
            "33     20\n",
            "28     20\n",
            "18     20\n",
            "31     19\n",
            "19     18\n",
            "39     18\n",
            "29     17\n",
            "40     16\n",
            "25     16\n",
            "26     16\n",
            "22     16\n",
            "37     16\n",
            "41     15\n",
            "35     15\n",
            "36     14\n",
            "15     14\n",
            "17     14\n",
            "20     13\n",
            "24     12\n",
            "42     11\n",
            "13     11\n",
            "21     10\n",
            "46      8\n",
            "34      8\n",
            "12      7\n",
            "38      7\n",
            "11      6\n",
            "43      6\n",
            "16      6\n",
            "45      6\n",
            "14      6\n",
            "44      5\n",
            "10      5\n",
            "48      4\n",
            "47      4\n",
            "49      3\n",
            "50      3\n",
            "8       2\n",
            "7       2\n",
            "52      2\n",
            "54      2\n",
            "63      1\n",
            "60      1\n",
            "56      1\n",
            "51      1\n",
            "99      1\n",
            "Name: skin, dtype: int64\n",
            "0      374\n",
            "105     11\n",
            "130      9\n",
            "140      9\n",
            "120      8\n",
            "      ... \n",
            "73       1\n",
            "171      1\n",
            "255      1\n",
            "52       1\n",
            "112      1\n",
            "Name: insulin, Length: 186, dtype: int64\n",
            "32.0    13\n",
            "31.6    12\n",
            "31.2    12\n",
            "0.0     11\n",
            "32.4    10\n",
            "        ..\n",
            "36.7     1\n",
            "41.8     1\n",
            "42.6     1\n",
            "42.8     1\n",
            "46.3     1\n",
            "Name: bmi, Length: 248, dtype: int64\n",
            "0.258    6\n",
            "0.254    6\n",
            "0.268    5\n",
            "0.207    5\n",
            "0.261    5\n",
            "        ..\n",
            "1.353    1\n",
            "0.655    1\n",
            "0.092    1\n",
            "0.926    1\n",
            "0.171    1\n",
            "Name: pedigree, Length: 517, dtype: int64\n",
            "22    72\n",
            "21    63\n",
            "25    48\n",
            "24    46\n",
            "23    38\n",
            "28    35\n",
            "26    33\n",
            "27    32\n",
            "29    29\n",
            "31    24\n",
            "41    22\n",
            "30    21\n",
            "37    19\n",
            "42    18\n",
            "33    17\n",
            "38    16\n",
            "36    16\n",
            "32    16\n",
            "45    15\n",
            "34    14\n",
            "46    13\n",
            "43    13\n",
            "40    13\n",
            "39    12\n",
            "35    10\n",
            "50     8\n",
            "51     8\n",
            "52     8\n",
            "44     8\n",
            "58     7\n",
            "47     6\n",
            "54     6\n",
            "49     5\n",
            "48     5\n",
            "57     5\n",
            "53     5\n",
            "60     5\n",
            "66     4\n",
            "63     4\n",
            "62     4\n",
            "55     4\n",
            "67     3\n",
            "56     3\n",
            "59     3\n",
            "65     3\n",
            "69     2\n",
            "61     2\n",
            "72     1\n",
            "81     1\n",
            "64     1\n",
            "70     1\n",
            "68     1\n",
            "Name: age, dtype: int64\n",
            "0    500\n",
            "1    268\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#exploring target variable\n",
        "diabetes_df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aSdrxxfZIl2",
        "outputId": "89aa3f19-2884-4358-caf4-175f2ec21ad3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    268\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for missing values in variables\n",
        "diabetes_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dd01mt4ZSOS",
        "outputId": "b26e6406-9a69-46a1-ab2c-80acec0dde63"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pregnant    0\n",
              "glucose     0\n",
              "bp          0\n",
              "skin        0\n",
              "insulin     0\n",
              "bmi         0\n",
              "pedigree    0\n",
              "age         0\n",
              "label       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#define feature vector and target variable\n",
        "X = diabetes_df.drop(['label'], axis=1) # Drop the target variable\n",
        "y = diabetes_df['label']"
      ],
      "metadata": {
        "id": "Fi7x3jN_ZXI9"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,random_state=1) # 75% training and 25% test\n",
        "X_train.shape, X_test.shape # Shapes of X_train and X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "89oli0-RZhIv",
        "outputId": "e4fc07fa-aa68-41c1-8893-2f7f8770af64"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((576, 8), (192, 8))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Feature Engineering : encoding categorical variables"
      ],
      "metadata": {
        "id": "8y7SBvZOZqb6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.dtypes # Check data types in X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQRrtLpuZnJN",
        "outputId": "e45d149b-f64e-403b-b69b-85f4e4fb5857"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pregnant      int64\n",
              "glucose       int64\n",
              "bp            int64\n",
              "skin          int64\n",
              "insulin       int64\n",
              "bmi         float64\n",
              "pedigree    float64\n",
              "age           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = OrdinalEncoder()\n",
        "\n",
        "X_train=encoder.fit_transform(X_train)\n",
        "X_test=encoder.fit_transform(X_test)"
      ],
      "metadata": {
        "id": "zIw2tdE8aJjL"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DTC=DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "DC0f09A1fHqr"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#building decision tree classifier with cini index criterion\n",
        "clf_gini=DecisionTreeClassifier(criterion='gini',max_depth=4,random_state=0)#create decision tree classifier object\n",
        "clf_gini.fit(X_train,y_train)#train the classifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "IEGcj0Efae4n",
        "outputId": "a5e9c5ce-89c2-4993-d0f0-3c598ab6f14b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=4, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4, random_state=0)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=4, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=clf_gini.predict(X_test)"
      ],
      "metadata": {
        "id": "SkOIhYCtcQ92"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#evaluating the model\n",
        "print('Accuracy:', metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odTAquRBcVgC",
        "outputId": "e6c49984-f784-4ab3-8a89-e45293a05e45"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.71875\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "conf_mat = confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "G6eOwQ3acd9Z"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(conf_mat,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "FFDvWxeEdOyb",
        "outputId": "6d141836-cecf-4169-eb33-48cf298c3a0c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "VWvtlFnPeAU_"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "params={\n",
        "    'criterion':['gini','entropy'],\n",
        "    'max_depth':[2,4,6,8,10,12],\n",
        "    'splitter':['best','random']\n",
        "}"
      ],
      "metadata": {
        "id": "3dJIUwwEeT2E"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf_GS=GridSearchCV(DTC,params)\n",
        "clf_GS.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "rS9P1W5we-EY",
        "outputId": "d260d474-78fd-46fa-c5c0-889479c5cb4f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=DecisionTreeClassifier(),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [2, 4, 6, 8, 10, 12],\n",
              "                         'splitter': ['best', 'random']})"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12],\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12],\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_GS.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRsUKrgefSPk",
        "outputId": "90f9d858-62fd-4230-8e89-703d6380064c"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini', 'max_depth': 2, 'splitter': 'random'}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#building decision tree classifier with cini index criterion\n",
        "clf_gini=DecisionTreeClassifier(criterion='gini',max_depth=2,splitter='random',random_state=0)#create decision tree classifier object\n",
        "clf_gini.fit(X_train,y_train)#train the cla//ssifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "2fb6c29c-38b5-44d6-ce15-8fff51e0f444",
        "id": "hRebPXRYf1Us"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=2, random_state=0, splitter='random')"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=2, random_state=0, splitter=&#x27;random&#x27;)</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=2, random_state=0, splitter=&#x27;random&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=clf_gini.predict(X_test)"
      ],
      "metadata": {
        "id": "bR9_T107f1Ut"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#evaluating the model\n",
        "print('Accuracy:', metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40aba39a-8ca3-48c5-93e5-08a2395b6062",
        "id": "tBrPZEv0f1Uu"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6510416666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "conf_mat = confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "I2gu3uhsf1Uu"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(conf_mat,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "e085f587-d098-4059-eea2-84d097cd135f",
        "id": "TyBTXvujf1Uv"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualizing decision trees\n",
        "from six import StringIO\n",
        "from IPython.display import Image\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus\n",
        "\n",
        "dot_data = StringIO()\n",
        "export_graphviz(clf_gini,out_file=dot_data,filled=True,rounded=True,special_characters=True,feature_names=X.columns,class_names=['0','1'])\n",
        "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
        "graph.write_png('diabetes.png')\n",
        "Image(graph.create_png())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "xgxIictBgR-9",
        "outputId": "71e86433-cc31-4baf-bdfd-aea7a3ec89d3"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOUZkNo7glMI",
        "outputId": "9aa1a33a-eb8c-4d76-8ed6-bb55e8db01e9"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      1.00      0.79       123\n",
            "           1       1.00      0.03      0.06        69\n",
            "\n",
            "    accuracy                           0.65       192\n",
            "   macro avg       0.82      0.51      0.42       192\n",
            "weighted avg       0.77      0.65      0.52       192\n",
            "\n"
          ]
        }
      ]
    }
  ]
}