Task_05_Sparks.ipynb

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      "name": "Task-05-Sparks.ipynb",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/parnika-dotcom/Sparks_Tasks/blob/main/Task_05_Sparks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vaj7XuuovEP"
      },
      "source": [
        "#**Data Science and Business Analytics Intern**\n",
        "\n",
        "##**The Sparks Foundation**\n",
        "\n",
        "**Task 5:- Prediction using Supervised Machine Learning by Decision Tree Algorithm(ML).**\n",
        "\n",
        "**Name: Parnika Sharma (GRIPMAR21 - Intern)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c66gb7RzpWjz"
      },
      "source": [
        "##**Aim:-**\n",
        "\n",
        "**● Create the Decision Tree classifier and visualize it graphically.**\n",
        "\n",
        "**● The purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wI851PoIppYh"
      },
      "source": [
        "##**Importing the libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otHhdjkloQI5"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix \n",
        "from sklearn.tree import DecisionTreeClassifier \n",
        "from sklearn.metrics import accuracy_score \n",
        "from sklearn.datasets import load_iris\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn import tree"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKYTRJgAp6IJ"
      },
      "source": [
        "##**Importing the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "G2ojxjCWp_QW",
        "outputId": "8f95bb30-2bde-44dc-bb38-402e8389546a"
      },
      "source": [
        "#Import the Iris Dataset\n",
        "df=pd.read_csv('/content/Iris (2).csv')\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
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        "id": "41PKCW3aqOK7",
        "outputId": "6e927750-c9c5-4d0c-8bfd-de8850de6fe5"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  ...  PetalWidthCm         Species\n",
              "145  146            6.7  ...           2.3  Iris-virginica\n",
              "146  147            6.3  ...           1.9  Iris-virginica\n",
              "147  148            6.5  ...           2.0  Iris-virginica\n",
              "148  149            6.2  ...           2.3  Iris-virginica\n",
              "149  150            5.9  ...           1.8  Iris-virginica\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrADHOsqqObl",
        "outputId": "c6510a93-cf4d-4a31-b190-a4c7d44aa61a"
      },
      "source": [
        "#find unique fields inside the species\n",
        "df['Species'].nunique()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyoXh96CqQ1m",
        "outputId": "6b837d65-87eb-44c1-bce9-a904f76e4c96"
      },
      "source": [
        "#type of the dataset\n",
        "print(type(df))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZUFJsBaqTXh",
        "outputId": "469e0528-0f8b-4b36-8391-3b3ac3a639e3"
      },
      "source": [
        "#type of unique species\n",
        "df['Species'].unique()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxmkM8Wpqeft",
        "outputId": "24a842c7-f786-4117-a51f-7cdd4685d35e"
      },
      "source": [
        "#Knowing the datatype of each attribute\n",
        "df.dtypes"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "SepalLengthCm    float64\n",
              "SepalWidthCm     float64\n",
              "PetalLengthCm    float64\n",
              "PetalWidthCm     float64\n",
              "Species           object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kb4JumMWqjv7",
        "outputId": "a3afa8a7-8c17-47f2-82c1-ee5356f92c95"
      },
      "source": [
        "#Shape of the Dataset\n",
        "df.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "4lohpj0xqkcD",
        "outputId": "c11a4624-8345-44f0-f27f-f6a181a0a3ff"
      },
      "source": [
        "#drop the value of id form dataset\n",
        "df.drop('Id',axis=1,inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5nkJucVqmDL",
        "outputId": "8fe2bcbb-9292-470f-a11d-29a47bd1a85d"
      },
      "source": [
        "#to summarize the dataframe\n",
        "df.info()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   SepalLengthCm  150 non-null    float64\n",
            " 1   SepalWidthCm   150 non-null    float64\n",
            " 2   PetalLengthCm  150 non-null    float64\n",
            " 3   PetalWidthCm   150 non-null    float64\n",
            " 4   Species        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MeGEPvpYq52r"
      },
      "source": [
        "##**Studying the Statistical properties of the Dataset**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "UHb5WX2fqo5b",
        "outputId": "6bfe7af1-b381-4d96-d8a7-6a60bd4bdaa3"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count     150.000000    150.000000     150.000000    150.000000\n",
              "mean        5.843333      3.054000       3.758667      1.198667\n",
              "std         0.828066      0.433594       1.764420      0.763161\n",
              "min         4.300000      2.000000       1.000000      0.100000\n",
              "25%         5.100000      2.800000       1.600000      0.300000\n",
              "50%         5.800000      3.000000       4.350000      1.300000\n",
              "75%         6.400000      3.300000       5.100000      1.800000\n",
              "max         7.900000      4.400000       6.900000      2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lZq013XrHV9",
        "outputId": "5bd20cf7-7fbc-422f-c6f2-06f3f6ccda4d"
      },
      "source": [
        "#to return the null values in the dataframe\n",
        "df.isnull().sum()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeoveC34rJWX",
        "outputId": "53159a41-3614-4cd1-9d6a-c1b63e8ba241"
      },
      "source": [
        "#printing Features name of iris data\n",
        "print('features',df.columns)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "features Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
            "       'Species'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LqF2Z4LKrPIl",
        "outputId": "9328afce-2012-490e-a42b-165226bf4426"
      },
      "source": [
        "print(df.keys())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
            "       'Species'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ThjDWKaZrRX3",
        "outputId": "d219403c-720f-45f1-f7eb-6b5eede4b1b4"
      },
      "source": [
        "#dividing the dataset in the target and sample format\n",
        "#priting samples and target \n",
        "X = df.values[:, 0:4] \n",
        "Y = df.values[:,-1]\n",
        "print(X)\n",
        "print(Y)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.0 1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.0 3.6 1.4 0.2]\n",
            " [5.4 3.9 1.7 0.4]\n",
            " [4.6 3.4 1.4 0.3]\n",
            " [5.0 3.4 1.5 0.2]\n",
            " [4.4 2.9 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.4 3.7 1.5 0.2]\n",
            " [4.8 3.4 1.6 0.2]\n",
            " [4.8 3.0 1.4 0.1]\n",
            " [4.3 3.0 1.1 0.1]\n",
            " [5.8 4.0 1.2 0.2]\n",
            " [5.7 4.4 1.5 0.4]\n",
            " [5.4 3.9 1.3 0.4]\n",
            " [5.1 3.5 1.4 0.3]\n",
            " [5.7 3.8 1.7 0.3]\n",
            " [5.1 3.8 1.5 0.3]\n",
            " [5.4 3.4 1.7 0.2]\n",
            " [5.1 3.7 1.5 0.4]\n",
            " [4.6 3.6 1.0 0.2]\n",
            " [5.1 3.3 1.7 0.5]\n",
            " [4.8 3.4 1.9 0.2]\n",
            " [5.0 3.0 1.6 0.2]\n",
            " [5.0 3.4 1.6 0.4]\n",
            " [5.2 3.5 1.5 0.2]\n",
            " [5.2 3.4 1.4 0.2]\n",
            " [4.7 3.2 1.6 0.2]\n",
            " [4.8 3.1 1.6 0.2]\n",
            " [5.4 3.4 1.5 0.4]\n",
            " [5.2 4.1 1.5 0.1]\n",
            " [5.5 4.2 1.4 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [5.0 3.2 1.2 0.2]\n",
            " [5.5 3.5 1.3 0.2]\n",
            " [4.9 3.1 1.5 0.1]\n",
            " [4.4 3.0 1.3 0.2]\n",
            " [5.1 3.4 1.5 0.2]\n",
            " [5.0 3.5 1.3 0.3]\n",
            " [4.5 2.3 1.3 0.3]\n",
            " [4.4 3.2 1.3 0.2]\n",
            " [5.0 3.5 1.6 0.6]\n",
            " [5.1 3.8 1.9 0.4]\n",
            " [4.8 3.0 1.4 0.3]\n",
            " [5.1 3.8 1.6 0.2]\n",
            " [4.6 3.2 1.4 0.2]\n",
            " [5.3 3.7 1.5 0.2]\n",
            " [5.0 3.3 1.4 0.2]\n",
            " [7.0 3.2 4.7 1.4]\n",
            " [6.4 3.2 4.5 1.5]\n",
            " [6.9 3.1 4.9 1.5]\n",
            " [5.5 2.3 4.0 1.3]\n",
            " [6.5 2.8 4.6 1.5]\n",
            " [5.7 2.8 4.5 1.3]\n",
            " [6.3 3.3 4.7 1.6]\n",
            " [4.9 2.4 3.3 1.0]\n",
            " [6.6 2.9 4.6 1.3]\n",
            " [5.2 2.7 3.9 1.4]\n",
            " [5.0 2.0 3.5 1.0]\n",
            " [5.9 3.0 4.2 1.5]\n",
            " [6.0 2.2 4.0 1.0]\n",
            " [6.1 2.9 4.7 1.4]\n",
            " [5.6 2.9 3.6 1.3]\n",
            " [6.7 3.1 4.4 1.4]\n",
            " [5.6 3.0 4.5 1.5]\n",
            " [5.8 2.7 4.1 1.0]\n",
            " [6.2 2.2 4.5 1.5]\n",
            " [5.6 2.5 3.9 1.1]\n",
            " [5.9 3.2 4.8 1.8]\n",
            " [6.1 2.8 4.0 1.3]\n",
            " [6.3 2.5 4.9 1.5]\n",
            " [6.1 2.8 4.7 1.2]\n",
            " [6.4 2.9 4.3 1.3]\n",
            " [6.6 3.0 4.4 1.4]\n",
            " [6.8 2.8 4.8 1.4]\n",
            " [6.7 3.0 5.0 1.7]\n",
            " [6.0 2.9 4.5 1.5]\n",
            " [5.7 2.6 3.5 1.0]\n",
            " [5.5 2.4 3.8 1.1]\n",
            " [5.5 2.4 3.7 1.0]\n",
            " [5.8 2.7 3.9 1.2]\n",
            " [6.0 2.7 5.1 1.6]\n",
            " [5.4 3.0 4.5 1.5]\n",
            " [6.0 3.4 4.5 1.6]\n",
            " [6.7 3.1 4.7 1.5]\n",
            " [6.3 2.3 4.4 1.3]\n",
            " [5.6 3.0 4.1 1.3]\n",
            " [5.5 2.5 4.0 1.3]\n",
            " [5.5 2.6 4.4 1.2]\n",
            " [6.1 3.0 4.6 1.4]\n",
            " [5.8 2.6 4.0 1.2]\n",
            " [5.0 2.3 3.3 1.0]\n",
            " [5.6 2.7 4.2 1.3]\n",
            " [5.7 3.0 4.2 1.2]\n",
            " [5.7 2.9 4.2 1.3]\n",
            " [6.2 2.9 4.3 1.3]\n",
            " [5.1 2.5 3.0 1.1]\n",
            " [5.7 2.8 4.1 1.3]\n",
            " [6.3 3.3 6.0 2.5]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [7.1 3.0 5.9 2.1]\n",
            " [6.3 2.9 5.6 1.8]\n",
            " [6.5 3.0 5.8 2.2]\n",
            " [7.6 3.0 6.6 2.1]\n",
            " [4.9 2.5 4.5 1.7]\n",
            " [7.3 2.9 6.3 1.8]\n",
            " [6.7 2.5 5.8 1.8]\n",
            " [7.2 3.6 6.1 2.5]\n",
            " [6.5 3.2 5.1 2.0]\n",
            " [6.4 2.7 5.3 1.9]\n",
            " [6.8 3.0 5.5 2.1]\n",
            " [5.7 2.5 5.0 2.0]\n",
            " [5.8 2.8 5.1 2.4]\n",
            " [6.4 3.2 5.3 2.3]\n",
            " [6.5 3.0 5.5 1.8]\n",
            " [7.7 3.8 6.7 2.2]\n",
            " [7.7 2.6 6.9 2.3]\n",
            " [6.0 2.2 5.0 1.5]\n",
            " [6.9 3.2 5.7 2.3]\n",
            " [5.6 2.8 4.9 2.0]\n",
            " [7.7 2.8 6.7 2.0]\n",
            " [6.3 2.7 4.9 1.8]\n",
            " [6.7 3.3 5.7 2.1]\n",
            " [7.2 3.2 6.0 1.8]\n",
            " [6.2 2.8 4.8 1.8]\n",
            " [6.1 3.0 4.9 1.8]\n",
            " [6.4 2.8 5.6 2.1]\n",
            " [7.2 3.0 5.8 1.6]\n",
            " [7.4 2.8 6.1 1.9]\n",
            " [7.9 3.8 6.4 2.0]\n",
            " [6.4 2.8 5.6 2.2]\n",
            " [6.3 2.8 5.1 1.5]\n",
            " [6.1 2.6 5.6 1.4]\n",
            " [7.7 3.0 6.1 2.3]\n",
            " [6.3 3.4 5.6 2.4]\n",
            " [6.4 3.1 5.5 1.8]\n",
            " [6.0 3.0 4.8 1.8]\n",
            " [6.9 3.1 5.4 2.1]\n",
            " [6.7 3.1 5.6 2.4]\n",
            " [6.9 3.1 5.1 2.3]\n",
            " [5.8 2.7 5.1 1.9]\n",
            " [6.8 3.2 5.9 2.3]\n",
            " [6.7 3.3 5.7 2.5]\n",
            " [6.7 3.0 5.2 2.3]\n",
            " [6.3 2.5 5.0 1.9]\n",
            " [6.5 3.0 5.2 2.0]\n",
            " [6.2 3.4 5.4 2.3]\n",
            " [5.9 3.0 5.1 1.8]]\n",
            "['Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7ofXRMmrakd"
      },
      "source": [
        "###**Splitting the dataset into train and test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoIuck-srU8P"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 100)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FG0Ds1OTrhfb"
      },
      "source": [
        "###**Decision tree classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7ZaLtlMrdqb"
      },
      "source": [
        "clf= DecisionTreeClassifier(random_state = 100)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vUyZgeckrmrN",
        "outputId": "b9ad4ad7-95e0-482f-e731-9afe7c878fbc"
      },
      "source": [
        "#fitting the training data\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=100, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJjrGYylrqgc",
        "outputId": "f9f3ea9e-65fd-40c3-8d3b-f72fe840e94c"
      },
      "source": [
        "#prediction on random data\n",
        "X=[[6.4,1.8 ,6.6 ,2.1]]\n",
        "Y_pred=clf.predict(X)\n",
        "print(Y_pred)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Iris-virginica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wNvQy4xFrv1i",
        "outputId": "145becc2-5a51-46f7-aa73-f6fe3b6e3400"
      },
      "source": [
        "#prediction on X_test (testing data )\n",
        "Y_pred=clf.predict(X_test)\n",
        "print(Y_pred)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
            " 'Iris-virginica' 'Iris-virginica' 'Iris-setosa' 'Iris-setosa'\n",
            " 'Iris-virginica' 'Iris-setosa' 'Iris-setosa' 'Iris-virginica'\n",
            " 'Iris-setosa' 'Iris-setosa' 'Iris-virginica' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-virginica' 'Iris-setosa' 'Iris-virginica' 'Iris-setosa'\n",
            " 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-setosa'\n",
            " 'Iris-versicolor' 'Iris-virginica' 'Iris-versicolor' 'Iris-versicolor'\n",
            " 'Iris-versicolor' 'Iris-setosa' 'Iris-setosa' 'Iris-versicolor'\n",
            " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-setosa' 'Iris-versicolor' 'Iris-virginica' 'Iris-virginica'\n",
            " 'Iris-setosa']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJO69jJSr15s"
      },
      "source": [
        "##**Checking the accuray of the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGTpshgUrzF0",
        "outputId": "7f68ea67-5a16-4b53-dc11-435cf14362e9"
      },
      "source": [
        "\n",
        "from sklearn.metrics import accuracy_score \n",
        "print(\"Accuracy:\",accuracy_score(y_test, Y_pred))\n",
        "from sklearn.metrics import  confusion_matrix\n",
        "cm=np.array(confusion_matrix(y_test,Y_pred))\n",
        "cm"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9555555555555556\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16,  0,  0],\n",
              "       [ 0, 10,  1],\n",
              "       [ 0,  1, 17]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "aQ4l8f1gs2Wm",
        "outputId": "21d6150a-7eb4-4e2e-9249-23f6daf76120"
      },
      "source": [
        "#Tree plotting \n",
        "tree.plot_tree(clf)\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(133.92000000000002, 195.696, 'X[2] <= 2.45\\ngini = 0.664\\nsamples = 105\\nvalue = [34, 39, 32]'),\n",
              " Text(100.44000000000001, 152.208, 'gini = 0.0\\nsamples = 34\\nvalue = [34, 0, 0]'),\n",
              " Text(167.40000000000003, 152.208, 'X[3] <= 1.65\\ngini = 0.495\\nsamples = 71\\nvalue = [0, 39, 32]'),\n",
              " Text(66.96000000000001, 108.72, 'X[2] <= 5.0\\ngini = 0.136\\nsamples = 41\\nvalue = [0, 38, 3]'),\n",
              " Text(33.480000000000004, 65.232, 'gini = 0.0\\nsamples = 37\\nvalue = [0, 37, 0]'),\n",
              " Text(100.44000000000001, 65.232, 'X[0] <= 6.05\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
              " Text(66.96000000000001, 21.744, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(133.92000000000002, 21.744, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
              " Text(267.84000000000003, 108.72, 'X[2] <= 4.85\\ngini = 0.064\\nsamples = 30\\nvalue = [0, 1, 29]'),\n",
              " Text(234.36, 65.232, 'X[1] <= 3.1\\ngini = 0.375\\nsamples = 4\\nvalue = [0, 1, 3]'),\n",
              " Text(200.88000000000002, 21.744, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
              " Text(267.84000000000003, 21.744, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(301.32000000000005, 65.232, 'gini = 0.0\\nsamples = 26\\nvalue = [0, 0, 26]')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZRMf9Tgs2r6",
        "outputId": "bf17b1d9-787f-4218-96cb-db2ea2b2cd5f"
      },
      "source": [
        "#Decision making in decision tree\n",
        "text_representation = tree.export_text(clf)\n",
        "print(text_representation)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "|--- feature_2 <= 2.45\n",
            "|   |--- class: Iris-setosa\n",
            "|--- feature_2 >  2.45\n",
            "|   |--- feature_3 <= 1.65\n",
            "|   |   |--- feature_2 <= 5.00\n",
            "|   |   |   |--- class: Iris-versicolor\n",
            "|   |   |--- feature_2 >  5.00\n",
            "|   |   |   |--- feature_0 <= 6.05\n",
            "|   |   |   |   |--- class: Iris-versicolor\n",
            "|   |   |   |--- feature_0 >  6.05\n",
            "|   |   |   |   |--- class: Iris-virginica\n",
            "|   |--- feature_3 >  1.65\n",
            "|   |   |--- feature_2 <= 4.85\n",
            "|   |   |   |--- feature_1 <= 3.10\n",
            "|   |   |   |   |--- class: Iris-virginica\n",
            "|   |   |   |--- feature_1 >  3.10\n",
            "|   |   |   |   |--- class: Iris-versicolor\n",
            "|   |   |--- feature_2 >  4.85\n",
            "|   |   |   |--- class: Iris-virginica\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m1OgtkZss939"
      },
      "source": [
        "#**----------END OF CODE------------**"
      ]
    }
  ]
}