Iris_flower_classification.ipynb

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      "source": [
        "Import the necessary libraries"
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      "source": [
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the Iris dataset."
      ],
      "metadata": {
        "id": "IsVlB9Q0S0Sv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "iris = pd.read_csv('/content/Iris.csv')\n"
      ],
      "metadata": {
        "id": "ejfOW9pVSmue"
      },
      "execution_count": 2,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "iris.head()"
      ],
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              "   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"
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              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
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              "      <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",
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              "      <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",
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              "      <th>2</th>\n",
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              "      <td>4.7</td>\n",
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              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "      <th>3</th>\n",
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              "      <th>4</th>\n",
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              "      <td>0.2</td>\n",
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            "text/plain": [
              "(150, 6)"
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          "execution_count": 4
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          "data": {
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
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              "      <td>150.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>5.100000</td>\n",
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              "      <td>1.600000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
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      "execution_count": 6,
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          "data": {
            "text/plain": [
              "<bound method DataFrame.info of       Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "0       Iris-setosa  \n",
              "1       Iris-setosa  \n",
              "2       Iris-setosa  \n",
              "3       Iris-setosa  \n",
              "4       Iris-setosa  \n",
              "..              ...  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  \n",
              "\n",
              "[150 rows x 6 columns]>"
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        "iris.isnull().sum()"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0\n",
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.Species.value_counts"
      ],
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        "colab": {
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        },
        "id": "6n2faSANVkuB",
        "outputId": "c8b90aa9-adb8-4354-a7fc-4c76bfcfee00"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method IndexOpsMixin.value_counts of 0         Iris-setosa\n",
              "1         Iris-setosa\n",
              "2         Iris-setosa\n",
              "3         Iris-setosa\n",
              "4         Iris-setosa\n",
              "            ...      \n",
              "145    Iris-virginica\n",
              "146    Iris-virginica\n",
              "147    Iris-virginica\n",
              "148    Iris-virginica\n",
              "149    Iris-virginica\n",
              "Name: Species, Length: 150, dtype: object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= iris[['Id','SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]\n",
        "y= iris['Species']"
      ],
      "metadata": {
        "id": "yYrowkPMVyve"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
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      },
      "execution_count": 10,
      "outputs": [
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            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "0      1            5.1           3.5            1.4           0.2\n",
              "1      2            4.9           3.0            1.4           0.2\n",
              "2      3            4.7           3.2            1.3           0.2\n",
              "3      4            4.6           3.1            1.5           0.2\n",
              "4      5            5.0           3.6            1.4           0.2\n",
              "..   ...            ...           ...            ...           ...\n",
              "145  146            6.7           3.0            5.2           2.3\n",
              "146  147            6.3           2.5            5.0           1.9\n",
              "147  148            6.5           3.0            5.2           2.0\n",
              "148  149            6.2           3.4            5.4           2.3\n",
              "149  150            5.9           3.0            5.1           1.8\n",
              "\n",
              "[150 rows x 5 columns]"
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              "      <th>147</th>\n",
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              "      <td>2.3</td>\n",
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              "    <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "y"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJ5ElVy7V5ok",
        "outputId": "780564c2-f486-40cc-df4b-eefd4102fed6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         Iris-setosa\n",
              "1         Iris-setosa\n",
              "2         Iris-setosa\n",
              "3         Iris-setosa\n",
              "4         Iris-setosa\n",
              "            ...      \n",
              "145    Iris-virginica\n",
              "146    Iris-virginica\n",
              "147    Iris-virginica\n",
              "148    Iris-virginica\n",
              "149    Iris-virginica\n",
              "Name: Species, Length: 150, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the data into a training set and a test set."
      ],
      "metadata": {
        "id": "-XsiltrxS3Yk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(iris.drop(\"Species\", axis=1), iris[\"Species\"], test_size=0.25)\n"
      ],
      "metadata": {
        "id": "KoVjuiGcSv8n"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a Decision Tree classifier."
      ],
      "metadata": {
        "id": "RVznjVJvTow2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_train, y_train)#Train the classifier on the training set.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "IAWPvN7HTssm",
        "outputId": "6bf386b3-5f52-4528-9556-eeac6f665924"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
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            ]
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          "metadata": {},
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        "Make predictions on the test set."
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        "id": "6vEjfEHsUAvn"
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      "cell_type": "code",
      "source": [
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "Scores = pd.DataFrame({'Actual':y_test,'Predictions':y_pred})\n",
        "Scores.head()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": 14,
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        {
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          "data": {
            "text/plain": [
              "             Actual     Predictions\n",
              "8       Iris-setosa     Iris-setosa\n",
              "144  Iris-virginica  Iris-virginica\n",
              "27      Iris-setosa     Iris-setosa\n",
              "132  Iris-virginica  Iris-virginica\n",
              "119  Iris-virginica  Iris-virginica"
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              "\n",
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              "      <th>27</th>\n",
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              "    <tr>\n",
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              "      <th>119</th>\n",
              "      <td>Iris-virginica</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-063e4e69-1e0a-4374-930e-fc14e266edde')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-063e4e69-1e0a-4374-930e-fc14e266edde button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-063e4e69-1e0a-4374-930e-fc14e266edde');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)#Evaluate the accuracy of the classifier."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sQqxAI_WXAh",
        "outputId": "47fa0a82-4a7a-4065-c8af-1512a1a25fcc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9736842105263158\n"
          ]
        }
      ]
    }
  ]
}