TASK_1_Iris_Flower_Classification.ipynb

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      "source": [
        "***IMPORTING NECESSARY LIBRARIES AND LOADING DATASET***"
      ],
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        "id": "aQFFltzqzsM4"
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              "      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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          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "df = pd.read_csv('/content/Iris.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***EXPLORATORY DATA ANALYSIS***"
      ],
      "metadata": {
        "id": "DHae0OdWz1dE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "YlJ-gvAlNvkK",
        "outputId": "c1e92cd6-bf1a-468b-8ab5-7af7183928fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
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            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.tail()"
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        "id": "d8otalyRNyxf",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\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",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  "
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-90c2a273-803d-4508-8994-549ebe775e7e 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-90c2a273-803d-4508-8994-549ebe775e7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cbc77f6e-7477-4b73-a523-8ebed1a20a57 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 146,\n        \"max\": 150,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          147,\n          150,\n          148\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30331501776206193,\n        \"min\": 5.9,\n        \"max\": 6.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.3,\n          5.9,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31937438845342625,\n        \"min\": 2.5,\n        \"max\": 3.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          2.5,\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14832396974191348,\n        \"min\": 5.0,\n        \"max\": 5.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          5.1,\n          5.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23021728866442667,\n        \"min\": 1.8,\n        \"max\": 2.3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.9,\n          1.8,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apvIfhWQ1URB",
        "outputId": "ce86d123-ec72-4803-a1e8-333c91685aa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNJE6E9r1ANT",
        "outputId": "4a9be78e-6df6-4f12-f6b4-3e246cdf0a4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "xRBeCnmmN9d4",
        "outputId": "7b6f1e07-929c-4ed5-c705-274b1d9270eb"
      },
      "execution_count": null,
      "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"
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              "    <tr>\n",
              "      <th>Id</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <td>float64</td>\n",
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              "      <td>object</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "_Handling missing data and Dropping duplicated data_"
      ],
      "metadata": {
        "id": "bMObWfxb0hF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "PAgbfCSPN2uw",
        "outputId": "676bf7ca-7400-479b-835d-d1443e696c4e"
      },
      "execution_count": null,
      "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"
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 33
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3P8MQCUn1E7T",
        "outputId": "ca47106f-1bcc-4c32-8e6c-2ae992d104f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kj-j4fsNOgUY",
        "outputId": "af673f76-4aa3-47e5-db0f-8ccb54de5a12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***EDA & DATA VISUALIZATION***"
      ],
      "metadata": {
        "id": "Hlm-FdXI1heE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***FEATURE ENGINEERING***"
      ],
      "metadata": {
        "id": "eZDtwIJl1oMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,:-1].values\n",
        "#x.ndim\n",
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVHGUaToO2lG",
        "outputId": "d286a177-9ac0-4a79-c6f8-4c333f2c6b77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "array([[1.00e+00, 5.10e+00, 3.50e+00, 1.40e+00, 2.00e-01],\n",
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              "       [3.00e+00, 4.70e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
              "       [4.00e+00, 4.60e+00, 3.10e+00, 1.50e+00, 2.00e-01],\n",
              "       [5.00e+00, 5.00e+00, 3.60e+00, 1.40e+00, 2.00e-01],\n",
              "       [6.00e+00, 5.40e+00, 3.90e+00, 1.70e+00, 4.00e-01],\n",
              "       [7.00e+00, 4.60e+00, 3.40e+00, 1.40e+00, 3.00e-01],\n",
              "       [8.00e+00, 5.00e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
              "       [9.00e+00, 4.40e+00, 2.90e+00, 1.40e+00, 2.00e-01],\n",
              "       [1.00e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [1.10e+01, 5.40e+00, 3.70e+00, 1.50e+00, 2.00e-01],\n",
              "       [1.20e+01, 4.80e+00, 3.40e+00, 1.60e+00, 2.00e-01],\n",
              "       [1.30e+01, 4.80e+00, 3.00e+00, 1.40e+00, 1.00e-01],\n",
              "       [1.40e+01, 4.30e+00, 3.00e+00, 1.10e+00, 1.00e-01],\n",
              "       [1.50e+01, 5.80e+00, 4.00e+00, 1.20e+00, 2.00e-01],\n",
              "       [1.60e+01, 5.70e+00, 4.40e+00, 1.50e+00, 4.00e-01],\n",
              "       [1.70e+01, 5.40e+00, 3.90e+00, 1.30e+00, 4.00e-01],\n",
              "       [1.80e+01, 5.10e+00, 3.50e+00, 1.40e+00, 3.00e-01],\n",
              "       [1.90e+01, 5.70e+00, 3.80e+00, 1.70e+00, 3.00e-01],\n",
              "       [2.00e+01, 5.10e+00, 3.80e+00, 1.50e+00, 3.00e-01],\n",
              "       [2.10e+01, 5.40e+00, 3.40e+00, 1.70e+00, 2.00e-01],\n",
              "       [2.20e+01, 5.10e+00, 3.70e+00, 1.50e+00, 4.00e-01],\n",
              "       [2.30e+01, 4.60e+00, 3.60e+00, 1.00e+00, 2.00e-01],\n",
              "       [2.40e+01, 5.10e+00, 3.30e+00, 1.70e+00, 5.00e-01],\n",
              "       [2.50e+01, 4.80e+00, 3.40e+00, 1.90e+00, 2.00e-01],\n",
              "       [2.60e+01, 5.00e+00, 3.00e+00, 1.60e+00, 2.00e-01],\n",
              "       [2.70e+01, 5.00e+00, 3.40e+00, 1.60e+00, 4.00e-01],\n",
              "       [2.80e+01, 5.20e+00, 3.50e+00, 1.50e+00, 2.00e-01],\n",
              "       [2.90e+01, 5.20e+00, 3.40e+00, 1.40e+00, 2.00e-01],\n",
              "       [3.00e+01, 4.70e+00, 3.20e+00, 1.60e+00, 2.00e-01],\n",
              "       [3.10e+01, 4.80e+00, 3.10e+00, 1.60e+00, 2.00e-01],\n",
              "       [3.20e+01, 5.40e+00, 3.40e+00, 1.50e+00, 4.00e-01],\n",
              "       [3.30e+01, 5.20e+00, 4.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [3.40e+01, 5.50e+00, 4.20e+00, 1.40e+00, 2.00e-01],\n",
              "       [3.50e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [3.60e+01, 5.00e+00, 3.20e+00, 1.20e+00, 2.00e-01],\n",
              "       [3.70e+01, 5.50e+00, 3.50e+00, 1.30e+00, 2.00e-01],\n",
              "       [3.80e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [3.90e+01, 4.40e+00, 3.00e+00, 1.30e+00, 2.00e-01],\n",
              "       [4.00e+01, 5.10e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
              "       [4.10e+01, 5.00e+00, 3.50e+00, 1.30e+00, 3.00e-01],\n",
              "       [4.20e+01, 4.50e+00, 2.30e+00, 1.30e+00, 3.00e-01],\n",
              "       [4.30e+01, 4.40e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
              "       [4.40e+01, 5.00e+00, 3.50e+00, 1.60e+00, 6.00e-01],\n",
              "       [4.50e+01, 5.10e+00, 3.80e+00, 1.90e+00, 4.00e-01],\n",
              "       [4.60e+01, 4.80e+00, 3.00e+00, 1.40e+00, 3.00e-01],\n",
              "       [4.70e+01, 5.10e+00, 3.80e+00, 1.60e+00, 2.00e-01],\n",
              "       [4.80e+01, 4.60e+00, 3.20e+00, 1.40e+00, 2.00e-01],\n",
              "       [4.90e+01, 5.30e+00, 3.70e+00, 1.50e+00, 2.00e-01],\n",
              "       [5.00e+01, 5.00e+00, 3.30e+00, 1.40e+00, 2.00e-01],\n",
              "       [5.10e+01, 7.00e+00, 3.20e+00, 4.70e+00, 1.40e+00],\n",
              "       [5.20e+01, 6.40e+00, 3.20e+00, 4.50e+00, 1.50e+00],\n",
              "       [5.30e+01, 6.90e+00, 3.10e+00, 4.90e+00, 1.50e+00],\n",
              "       [5.40e+01, 5.50e+00, 2.30e+00, 4.00e+00, 1.30e+00],\n",
              "       [5.50e+01, 6.50e+00, 2.80e+00, 4.60e+00, 1.50e+00],\n",
              "       [5.60e+01, 5.70e+00, 2.80e+00, 4.50e+00, 1.30e+00],\n",
              "       [5.70e+01, 6.30e+00, 3.30e+00, 4.70e+00, 1.60e+00],\n",
              "       [5.80e+01, 4.90e+00, 2.40e+00, 3.30e+00, 1.00e+00],\n",
              "       [5.90e+01, 6.60e+00, 2.90e+00, 4.60e+00, 1.30e+00],\n",
              "       [6.00e+01, 5.20e+00, 2.70e+00, 3.90e+00, 1.40e+00],\n",
              "       [6.10e+01, 5.00e+00, 2.00e+00, 3.50e+00, 1.00e+00],\n",
              "       [6.20e+01, 5.90e+00, 3.00e+00, 4.20e+00, 1.50e+00],\n",
              "       [6.30e+01, 6.00e+00, 2.20e+00, 4.00e+00, 1.00e+00],\n",
              "       [6.40e+01, 6.10e+00, 2.90e+00, 4.70e+00, 1.40e+00],\n",
              "       [6.50e+01, 5.60e+00, 2.90e+00, 3.60e+00, 1.30e+00],\n",
              "       [6.60e+01, 6.70e+00, 3.10e+00, 4.40e+00, 1.40e+00],\n",
              "       [6.70e+01, 5.60e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
              "       [6.80e+01, 5.80e+00, 2.70e+00, 4.10e+00, 1.00e+00],\n",
              "       [6.90e+01, 6.20e+00, 2.20e+00, 4.50e+00, 1.50e+00],\n",
              "       [7.00e+01, 5.60e+00, 2.50e+00, 3.90e+00, 1.10e+00],\n",
              "       [7.10e+01, 5.90e+00, 3.20e+00, 4.80e+00, 1.80e+00],\n",
              "       [7.20e+01, 6.10e+00, 2.80e+00, 4.00e+00, 1.30e+00],\n",
              "       [7.30e+01, 6.30e+00, 2.50e+00, 4.90e+00, 1.50e+00],\n",
              "       [7.40e+01, 6.10e+00, 2.80e+00, 4.70e+00, 1.20e+00],\n",
              "       [7.50e+01, 6.40e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
              "       [7.60e+01, 6.60e+00, 3.00e+00, 4.40e+00, 1.40e+00],\n",
              "       [7.70e+01, 6.80e+00, 2.80e+00, 4.80e+00, 1.40e+00],\n",
              "       [7.80e+01, 6.70e+00, 3.00e+00, 5.00e+00, 1.70e+00],\n",
              "       [7.90e+01, 6.00e+00, 2.90e+00, 4.50e+00, 1.50e+00],\n",
              "       [8.00e+01, 5.70e+00, 2.60e+00, 3.50e+00, 1.00e+00],\n",
              "       [8.10e+01, 5.50e+00, 2.40e+00, 3.80e+00, 1.10e+00],\n",
              "       [8.20e+01, 5.50e+00, 2.40e+00, 3.70e+00, 1.00e+00],\n",
              "       [8.30e+01, 5.80e+00, 2.70e+00, 3.90e+00, 1.20e+00],\n",
              "       [8.40e+01, 6.00e+00, 2.70e+00, 5.10e+00, 1.60e+00],\n",
              "       [8.50e+01, 5.40e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
              "       [8.60e+01, 6.00e+00, 3.40e+00, 4.50e+00, 1.60e+00],\n",
              "       [8.70e+01, 6.70e+00, 3.10e+00, 4.70e+00, 1.50e+00],\n",
              "       [8.80e+01, 6.30e+00, 2.30e+00, 4.40e+00, 1.30e+00],\n",
              "       [8.90e+01, 5.60e+00, 3.00e+00, 4.10e+00, 1.30e+00],\n",
              "       [9.00e+01, 5.50e+00, 2.50e+00, 4.00e+00, 1.30e+00],\n",
              "       [9.10e+01, 5.50e+00, 2.60e+00, 4.40e+00, 1.20e+00],\n",
              "       [9.20e+01, 6.10e+00, 3.00e+00, 4.60e+00, 1.40e+00],\n",
              "       [9.30e+01, 5.80e+00, 2.60e+00, 4.00e+00, 1.20e+00],\n",
              "       [9.40e+01, 5.00e+00, 2.30e+00, 3.30e+00, 1.00e+00],\n",
              "       [9.50e+01, 5.60e+00, 2.70e+00, 4.20e+00, 1.30e+00],\n",
              "       [9.60e+01, 5.70e+00, 3.00e+00, 4.20e+00, 1.20e+00],\n",
              "       [9.70e+01, 5.70e+00, 2.90e+00, 4.20e+00, 1.30e+00],\n",
              "       [9.80e+01, 6.20e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
              "       [9.90e+01, 5.10e+00, 2.50e+00, 3.00e+00, 1.10e+00],\n",
              "       [1.00e+02, 5.70e+00, 2.80e+00, 4.10e+00, 1.30e+00],\n",
              "       [1.01e+02, 6.30e+00, 3.30e+00, 6.00e+00, 2.50e+00],\n",
              "       [1.02e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
              "       [1.03e+02, 7.10e+00, 3.00e+00, 5.90e+00, 2.10e+00],\n",
              "       [1.04e+02, 6.30e+00, 2.90e+00, 5.60e+00, 1.80e+00],\n",
              "       [1.05e+02, 6.50e+00, 3.00e+00, 5.80e+00, 2.20e+00],\n",
              "       [1.06e+02, 7.60e+00, 3.00e+00, 6.60e+00, 2.10e+00],\n",
              "       [1.07e+02, 4.90e+00, 2.50e+00, 4.50e+00, 1.70e+00],\n",
              "       [1.08e+02, 7.30e+00, 2.90e+00, 6.30e+00, 1.80e+00],\n",
              "       [1.09e+02, 6.70e+00, 2.50e+00, 5.80e+00, 1.80e+00],\n",
              "       [1.10e+02, 7.20e+00, 3.60e+00, 6.10e+00, 2.50e+00],\n",
              "       [1.11e+02, 6.50e+00, 3.20e+00, 5.10e+00, 2.00e+00],\n",
              "       [1.12e+02, 6.40e+00, 2.70e+00, 5.30e+00, 1.90e+00],\n",
              "       [1.13e+02, 6.80e+00, 3.00e+00, 5.50e+00, 2.10e+00],\n",
              "       [1.14e+02, 5.70e+00, 2.50e+00, 5.00e+00, 2.00e+00],\n",
              "       [1.15e+02, 5.80e+00, 2.80e+00, 5.10e+00, 2.40e+00],\n",
              "       [1.16e+02, 6.40e+00, 3.20e+00, 5.30e+00, 2.30e+00],\n",
              "       [1.17e+02, 6.50e+00, 3.00e+00, 5.50e+00, 1.80e+00],\n",
              "       [1.18e+02, 7.70e+00, 3.80e+00, 6.70e+00, 2.20e+00],\n",
              "       [1.19e+02, 7.70e+00, 2.60e+00, 6.90e+00, 2.30e+00],\n",
              "       [1.20e+02, 6.00e+00, 2.20e+00, 5.00e+00, 1.50e+00],\n",
              "       [1.21e+02, 6.90e+00, 3.20e+00, 5.70e+00, 2.30e+00],\n",
              "       [1.22e+02, 5.60e+00, 2.80e+00, 4.90e+00, 2.00e+00],\n",
              "       [1.23e+02, 7.70e+00, 2.80e+00, 6.70e+00, 2.00e+00],\n",
              "       [1.24e+02, 6.30e+00, 2.70e+00, 4.90e+00, 1.80e+00],\n",
              "       [1.25e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.10e+00],\n",
              "       [1.26e+02, 7.20e+00, 3.20e+00, 6.00e+00, 1.80e+00],\n",
              "       [1.27e+02, 6.20e+00, 2.80e+00, 4.80e+00, 1.80e+00],\n",
              "       [1.28e+02, 6.10e+00, 3.00e+00, 4.90e+00, 1.80e+00],\n",
              "       [1.29e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.10e+00],\n",
              "       [1.30e+02, 7.20e+00, 3.00e+00, 5.80e+00, 1.60e+00],\n",
              "       [1.31e+02, 7.40e+00, 2.80e+00, 6.10e+00, 1.90e+00],\n",
              "       [1.32e+02, 7.90e+00, 3.80e+00, 6.40e+00, 2.00e+00],\n",
              "       [1.33e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.20e+00],\n",
              "       [1.34e+02, 6.30e+00, 2.80e+00, 5.10e+00, 1.50e+00],\n",
              "       [1.35e+02, 6.10e+00, 2.60e+00, 5.60e+00, 1.40e+00],\n",
              "       [1.36e+02, 7.70e+00, 3.00e+00, 6.10e+00, 2.30e+00],\n",
              "       [1.37e+02, 6.30e+00, 3.40e+00, 5.60e+00, 2.40e+00],\n",
              "       [1.38e+02, 6.40e+00, 3.10e+00, 5.50e+00, 1.80e+00],\n",
              "       [1.39e+02, 6.00e+00, 3.00e+00, 4.80e+00, 1.80e+00],\n",
              "       [1.40e+02, 6.90e+00, 3.10e+00, 5.40e+00, 2.10e+00],\n",
              "       [1.41e+02, 6.70e+00, 3.10e+00, 5.60e+00, 2.40e+00],\n",
              "       [1.42e+02, 6.90e+00, 3.10e+00, 5.10e+00, 2.30e+00],\n",
              "       [1.43e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
              "       [1.44e+02, 6.80e+00, 3.20e+00, 5.90e+00, 2.30e+00],\n",
              "       [1.45e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.50e+00],\n",
              "       [1.46e+02, 6.70e+00, 3.00e+00, 5.20e+00, 2.30e+00],\n",
              "       [1.47e+02, 6.30e+00, 2.50e+00, 5.00e+00, 1.90e+00],\n",
              "       [1.48e+02, 6.50e+00, 3.00e+00, 5.20e+00, 2.00e+00],\n",
              "       [1.49e+02, 6.20e+00, 3.40e+00, 5.40e+00, 2.30e+00],\n",
              "       [1.50e+02, 5.90e+00, 3.00e+00, 5.10e+00, 1.80e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y=df.iloc[:,-1].values\n",
        "#y.ndim\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HzqUo7pnT0Nz",
        "outputId": "72104230-3aef-438f-c64a-34f38200a376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training and Testing data\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30,random_state=42)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGlny1tnT6jR",
        "outputId": "bf2d3635-90aa-496a-fb44-c2f5c52c8ba8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[8.20e+01, 5.50e+00, 2.40e+00, 3.70e+00, 1.00e+00],\n",
              "       [1.34e+02, 6.30e+00, 2.80e+00, 5.10e+00, 1.50e+00],\n",
              "       [1.38e+02, 6.40e+00, 3.10e+00, 5.50e+00, 1.80e+00],\n",
              "       [7.60e+01, 6.60e+00, 3.00e+00, 4.40e+00, 1.40e+00],\n",
              "       [1.10e+02, 7.20e+00, 3.60e+00, 6.10e+00, 2.50e+00],\n",
              "       [9.70e+01, 5.70e+00, 2.90e+00, 4.20e+00, 1.30e+00],\n",
              "       [1.06e+02, 7.60e+00, 3.00e+00, 6.60e+00, 2.10e+00],\n",
              "       [6.70e+01, 5.60e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
              "       [1.00e+00, 5.10e+00, 3.50e+00, 1.40e+00, 2.00e-01],\n",
              "       [1.23e+02, 7.70e+00, 2.80e+00, 6.70e+00, 2.00e+00],\n",
              "       [6.80e+01, 5.80e+00, 2.70e+00, 4.10e+00, 1.00e+00],\n",
              "       [2.90e+01, 5.20e+00, 3.40e+00, 1.40e+00, 2.00e-01],\n",
              "       [4.10e+01, 5.00e+00, 3.50e+00, 1.30e+00, 3.00e-01],\n",
              "       [4.50e+01, 5.10e+00, 3.80e+00, 1.90e+00, 4.00e-01],\n",
              "       [6.10e+01, 5.00e+00, 2.00e+00, 3.50e+00, 1.00e+00],\n",
              "       [1.24e+02, 6.30e+00, 2.70e+00, 4.90e+00, 1.80e+00],\n",
              "       [2.50e+01, 4.80e+00, 3.40e+00, 1.90e+00, 2.00e-01],\n",
              "       [2.60e+01, 5.00e+00, 3.00e+00, 1.60e+00, 2.00e-01],\n",
              "       [2.40e+01, 5.10e+00, 3.30e+00, 1.70e+00, 5.00e-01],\n",
              "       [9.50e+01, 5.60e+00, 2.70e+00, 4.20e+00, 1.30e+00],\n",
              "       [4.00e+01, 5.10e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
              "       [9.60e+01, 5.70e+00, 3.00e+00, 4.20e+00, 1.20e+00],\n",
              "       [1.18e+02, 7.70e+00, 3.80e+00, 6.70e+00, 2.20e+00],\n",
              "       [4.80e+01, 4.60e+00, 3.20e+00, 1.40e+00, 2.00e-01],\n",
              "       [9.80e+01, 6.20e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
              "       [1.14e+02, 5.70e+00, 2.50e+00, 5.00e+00, 2.00e+00],\n",
              "       [3.40e+01, 5.50e+00, 4.20e+00, 1.40e+00, 2.00e-01],\n",
              "       [1.39e+02, 6.00e+00, 3.00e+00, 4.80e+00, 1.80e+00],\n",
              "       [1.02e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
              "       [6.30e+01, 6.00e+00, 2.20e+00, 4.00e+00, 1.00e+00],\n",
              "       [8.50e+01, 5.40e+00, 3.00e+00, 4.50e+00, 1.50e+00],\n",
              "       [1.49e+02, 6.20e+00, 3.40e+00, 5.40e+00, 2.30e+00],\n",
              "       [5.40e+01, 5.50e+00, 2.30e+00, 4.00e+00, 1.30e+00],\n",
              "       [6.00e+00, 5.40e+00, 3.90e+00, 1.70e+00, 4.00e-01],\n",
              "       [9.40e+01, 5.00e+00, 2.30e+00, 3.30e+00, 1.00e+00],\n",
              "       [1.12e+02, 6.40e+00, 2.70e+00, 5.30e+00, 1.90e+00],\n",
              "       [5.00e+01, 5.00e+00, 3.30e+00, 1.40e+00, 2.00e-01],\n",
              "       [3.60e+01, 5.00e+00, 3.20e+00, 1.20e+00, 2.00e-01],\n",
              "       [8.10e+01, 5.50e+00, 2.40e+00, 3.80e+00, 1.10e+00],\n",
              "       [7.80e+01, 6.70e+00, 3.00e+00, 5.00e+00, 1.70e+00],\n",
              "       [3.50e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [1.15e+02, 5.80e+00, 2.80e+00, 5.10e+00, 2.40e+00],\n",
              "       [8.00e+00, 5.00e+00, 3.40e+00, 1.50e+00, 2.00e-01],\n",
              "       [4.40e+01, 5.00e+00, 3.50e+00, 1.60e+00, 6.00e-01],\n",
              "       [7.10e+01, 5.90e+00, 3.20e+00, 4.80e+00, 1.80e+00],\n",
              "       [9.90e+01, 5.10e+00, 2.50e+00, 3.00e+00, 1.10e+00],\n",
              "       [1.21e+02, 6.90e+00, 3.20e+00, 5.70e+00, 2.30e+00],\n",
              "       [8.40e+01, 6.00e+00, 2.70e+00, 5.10e+00, 1.60e+00],\n",
              "       [1.35e+02, 6.10e+00, 2.60e+00, 5.60e+00, 1.40e+00],\n",
              "       [1.36e+02, 7.70e+00, 3.00e+00, 6.10e+00, 2.30e+00],\n",
              "       [9.00e+01, 5.50e+00, 2.50e+00, 4.00e+00, 1.30e+00],\n",
              "       [9.00e+00, 4.40e+00, 2.90e+00, 1.40e+00, 2.00e-01],\n",
              "       [1.40e+01, 4.30e+00, 3.00e+00, 1.10e+00, 1.00e-01],\n",
              "       [1.20e+02, 6.00e+00, 2.20e+00, 5.00e+00, 1.50e+00],\n",
              "       [1.26e+02, 7.20e+00, 3.20e+00, 6.00e+00, 1.80e+00],\n",
              "       [4.00e+00, 4.60e+00, 3.10e+00, 1.50e+00, 2.00e-01],\n",
              "       [1.80e+01, 5.10e+00, 3.50e+00, 1.40e+00, 3.00e-01],\n",
              "       [3.90e+01, 4.40e+00, 3.00e+00, 1.30e+00, 2.00e-01],\n",
              "       [7.30e+01, 6.30e+00, 2.50e+00, 4.90e+00, 1.50e+00],\n",
              "       [1.37e+02, 6.30e+00, 3.40e+00, 5.60e+00, 2.40e+00],\n",
              "       [7.00e+00, 4.60e+00, 3.40e+00, 1.40e+00, 3.00e-01],\n",
              "       [1.13e+02, 6.80e+00, 3.00e+00, 5.50e+00, 2.10e+00],\n",
              "       [1.01e+02, 6.30e+00, 3.30e+00, 6.00e+00, 2.50e+00],\n",
              "       [3.00e+00, 4.70e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
              "       [6.40e+01, 6.10e+00, 2.90e+00, 4.70e+00, 1.40e+00],\n",
              "       [5.50e+01, 6.50e+00, 2.80e+00, 4.60e+00, 1.50e+00],\n",
              "       [1.27e+02, 6.20e+00, 2.80e+00, 4.80e+00, 1.80e+00],\n",
              "       [5.10e+01, 7.00e+00, 3.20e+00, 4.70e+00, 1.40e+00],\n",
              "       [1.16e+02, 6.40e+00, 3.20e+00, 5.30e+00, 2.30e+00],\n",
              "       [4.70e+01, 5.10e+00, 3.80e+00, 1.60e+00, 2.00e-01],\n",
              "       [1.40e+02, 6.90e+00, 3.10e+00, 5.40e+00, 2.10e+00],\n",
              "       [6.20e+01, 5.90e+00, 3.00e+00, 4.20e+00, 1.50e+00],\n",
              "       [1.48e+02, 6.50e+00, 3.00e+00, 5.20e+00, 2.00e+00],\n",
              "       [8.00e+01, 5.70e+00, 2.60e+00, 3.50e+00, 1.00e+00],\n",
              "       [6.00e+01, 5.20e+00, 2.70e+00, 3.90e+00, 1.40e+00],\n",
              "       [9.20e+01, 6.10e+00, 3.00e+00, 4.60e+00, 1.40e+00],\n",
              "       [4.20e+01, 4.50e+00, 2.30e+00, 1.30e+00, 3.00e-01],\n",
              "       [5.90e+01, 6.60e+00, 2.90e+00, 4.60e+00, 1.30e+00],\n",
              "       [9.10e+01, 5.50e+00, 2.60e+00, 4.40e+00, 1.20e+00],\n",
              "       [4.90e+01, 5.30e+00, 3.70e+00, 1.50e+00, 2.00e-01],\n",
              "       [8.90e+01, 5.60e+00, 3.00e+00, 4.10e+00, 1.30e+00],\n",
              "       [1.08e+02, 7.30e+00, 2.90e+00, 6.30e+00, 1.80e+00],\n",
              "       [1.25e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.10e+00],\n",
              "       [2.20e+01, 5.10e+00, 3.70e+00, 1.50e+00, 4.00e-01],\n",
              "       [5.80e+01, 4.90e+00, 2.40e+00, 3.30e+00, 1.00e+00],\n",
              "       [1.45e+02, 6.70e+00, 3.30e+00, 5.70e+00, 2.50e+00],\n",
              "       [1.30e+02, 7.20e+00, 3.00e+00, 5.80e+00, 1.60e+00],\n",
              "       [3.80e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [1.41e+02, 6.70e+00, 3.10e+00, 5.60e+00, 2.40e+00],\n",
              "       [2.00e+00, 4.90e+00, 3.00e+00, 1.40e+00, 2.00e-01],\n",
              "       [5.30e+01, 6.90e+00, 3.10e+00, 4.90e+00, 1.50e+00],\n",
              "       [1.31e+02, 7.40e+00, 2.80e+00, 6.10e+00, 1.90e+00],\n",
              "       [1.04e+02, 6.30e+00, 2.90e+00, 5.60e+00, 1.80e+00],\n",
              "       [1.00e+02, 5.70e+00, 2.80e+00, 4.10e+00, 1.30e+00],\n",
              "       [1.17e+02, 6.50e+00, 3.00e+00, 5.50e+00, 1.80e+00],\n",
              "       [8.80e+01, 6.30e+00, 2.30e+00, 4.40e+00, 1.30e+00],\n",
              "       [7.50e+01, 6.40e+00, 2.90e+00, 4.30e+00, 1.30e+00],\n",
              "       [1.22e+02, 5.60e+00, 2.80e+00, 4.90e+00, 2.00e+00],\n",
              "       [1.50e+02, 5.90e+00, 3.00e+00, 5.10e+00, 1.80e+00],\n",
              "       [2.10e+01, 5.40e+00, 3.40e+00, 1.70e+00, 2.00e-01],\n",
              "       [7.20e+01, 6.10e+00, 2.80e+00, 4.00e+00, 1.30e+00],\n",
              "       [1.07e+02, 4.90e+00, 2.50e+00, 4.50e+00, 1.70e+00],\n",
              "       [1.50e+01, 5.80e+00, 4.00e+00, 1.20e+00, 2.00e-01],\n",
              "       [9.30e+01, 5.80e+00, 2.60e+00, 4.00e+00, 1.20e+00],\n",
              "       [1.03e+02, 7.10e+00, 3.00e+00, 5.90e+00, 2.10e+00]])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sz7L2xCYUAbq",
        "outputId": "af37497b-b5a5-462f-8714-23365ef93775"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.40e+01, 6.10e+00, 2.80e+00, 4.70e+00, 1.20e+00],\n",
              "       [1.90e+01, 5.70e+00, 3.80e+00, 1.70e+00, 3.00e-01],\n",
              "       [1.19e+02, 7.70e+00, 2.60e+00, 6.90e+00, 2.30e+00],\n",
              "       [7.90e+01, 6.00e+00, 2.90e+00, 4.50e+00, 1.50e+00],\n",
              "       [7.70e+01, 6.80e+00, 2.80e+00, 4.80e+00, 1.40e+00],\n",
              "       [3.20e+01, 5.40e+00, 3.40e+00, 1.50e+00, 4.00e-01],\n",
              "       [6.50e+01, 5.60e+00, 2.90e+00, 3.60e+00, 1.30e+00],\n",
              "       [1.42e+02, 6.90e+00, 3.10e+00, 5.10e+00, 2.30e+00],\n",
              "       [6.90e+01, 6.20e+00, 2.20e+00, 4.50e+00, 1.50e+00],\n",
              "       [8.30e+01, 5.80e+00, 2.70e+00, 3.90e+00, 1.20e+00],\n",
              "       [1.11e+02, 6.50e+00, 3.20e+00, 5.10e+00, 2.00e+00],\n",
              "       [1.30e+01, 4.80e+00, 3.00e+00, 1.40e+00, 1.00e-01],\n",
              "       [3.70e+01, 5.50e+00, 3.50e+00, 1.30e+00, 2.00e-01],\n",
              "       [1.00e+01, 4.90e+00, 3.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [2.00e+01, 5.10e+00, 3.80e+00, 1.50e+00, 3.00e-01],\n",
              "       [5.70e+01, 6.30e+00, 3.30e+00, 4.70e+00, 1.60e+00],\n",
              "       [1.05e+02, 6.50e+00, 3.00e+00, 5.80e+00, 2.20e+00],\n",
              "       [7.00e+01, 5.60e+00, 2.50e+00, 3.90e+00, 1.10e+00],\n",
              "       [5.60e+01, 5.70e+00, 2.80e+00, 4.50e+00, 1.30e+00],\n",
              "       [1.33e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.20e+00],\n",
              "       [3.00e+01, 4.70e+00, 3.20e+00, 1.60e+00, 2.00e-01],\n",
              "       [1.28e+02, 6.10e+00, 3.00e+00, 4.90e+00, 1.80e+00],\n",
              "       [2.70e+01, 5.00e+00, 3.40e+00, 1.60e+00, 4.00e-01],\n",
              "       [1.29e+02, 6.40e+00, 2.80e+00, 5.60e+00, 2.10e+00],\n",
              "       [1.32e+02, 7.90e+00, 3.80e+00, 6.40e+00, 2.00e+00],\n",
              "       [1.46e+02, 6.70e+00, 3.00e+00, 5.20e+00, 2.30e+00],\n",
              "       [1.09e+02, 6.70e+00, 2.50e+00, 5.80e+00, 1.80e+00],\n",
              "       [1.44e+02, 6.80e+00, 3.20e+00, 5.90e+00, 2.30e+00],\n",
              "       [4.60e+01, 4.80e+00, 3.00e+00, 1.40e+00, 3.00e-01],\n",
              "       [3.10e+01, 4.80e+00, 3.10e+00, 1.60e+00, 2.00e-01],\n",
              "       [2.30e+01, 4.60e+00, 3.60e+00, 1.00e+00, 2.00e-01],\n",
              "       [1.60e+01, 5.70e+00, 4.40e+00, 1.50e+00, 4.00e-01],\n",
              "       [6.60e+01, 6.70e+00, 3.10e+00, 4.40e+00, 1.40e+00],\n",
              "       [1.20e+01, 4.80e+00, 3.40e+00, 1.60e+00, 2.00e-01],\n",
              "       [4.30e+01, 4.40e+00, 3.20e+00, 1.30e+00, 2.00e-01],\n",
              "       [1.47e+02, 6.30e+00, 2.50e+00, 5.00e+00, 1.90e+00],\n",
              "       [5.20e+01, 6.40e+00, 3.20e+00, 4.50e+00, 1.50e+00],\n",
              "       [2.80e+01, 5.20e+00, 3.50e+00, 1.50e+00, 2.00e-01],\n",
              "       [5.00e+00, 5.00e+00, 3.60e+00, 1.40e+00, 2.00e-01],\n",
              "       [3.30e+01, 5.20e+00, 4.10e+00, 1.50e+00, 1.00e-01],\n",
              "       [1.43e+02, 5.80e+00, 2.70e+00, 5.10e+00, 1.90e+00],\n",
              "       [8.60e+01, 6.00e+00, 3.40e+00, 4.50e+00, 1.60e+00],\n",
              "       [8.70e+01, 6.70e+00, 3.10e+00, 4.70e+00, 1.50e+00],\n",
              "       [1.70e+01, 5.40e+00, 3.90e+00, 1.30e+00, 4.00e-01],\n",
              "       [1.10e+01, 5.40e+00, 3.70e+00, 1.50e+00, 2.00e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "54jHsxuLUHKH",
        "outputId": "3f89e3fc-4bb3-46af-9036-2cc94b6444fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-virginica', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-virginica', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qx6-IblbUQZ8",
        "outputId": "3256dd7c-5d4f-4daa-8a46-ba503eea61ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Normalization\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "scaler.fit(x_train)\n",
        "x_train=scaler.transform(x_train)\n",
        "x_test=scaler.transform(x_test)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDp9shvfURvZ",
        "outputId": "6a530821-d763-44fd-f864-c8f823be8c50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.07890874, -0.4134164 , -1.46440146, -0.10014569, -0.32149987],\n",
              "       [ 1.32052747,  0.55122187, -0.49582097,  0.71771076,  0.35364985],\n",
              "       [ 1.41603661,  0.67180165,  0.2306144 ,  0.95138404,  0.75873969],\n",
              "       [-0.06435496,  0.91296121, -0.01153072,  0.30878254,  0.21861991],\n",
              "       [ 0.74747267,  1.63643991,  1.44134002,  1.30189395,  1.7039493 ],\n",
              "       [ 0.43706799, -0.17225683, -0.25367584,  0.1919459 ,  0.08358997],\n",
              "       [ 0.65196354,  2.11875905, -0.01153072,  1.59398554,  1.16382952],\n",
              "       [-0.27925051, -0.29283662, -0.01153072,  0.36720086,  0.35364985],\n",
              "       [-1.85515122, -0.89573553,  1.19919489, -1.443767  , -1.40173942],\n",
              "       [ 1.05787735,  2.23933883, -0.49582097,  1.65240385,  1.02879957],\n",
              "       [-0.25537323, -0.05167705, -0.73796609,  0.13352758, -0.32149987],\n",
              "       [-1.18658728, -0.77515575,  0.95704977, -1.443767  , -1.40173942],\n",
              "       [-0.90005988, -1.01631531,  1.19919489, -1.50218532, -1.26670948],\n",
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              "       [-1.25821913, -1.01631531, -0.01153072, -1.32693037, -1.40173942],\n",
              "       [-1.3059737 , -0.89573553,  0.71490465, -1.26851205, -0.99664959],\n",
              "       [ 0.38931342, -0.29283662, -0.73796609,  0.1919459 ,  0.08358997],\n",
              "       [-0.92393717, -0.89573553,  0.95704977, -1.38534869, -1.40173942],\n",
              "       [ 0.4131907 , -0.17225683, -0.01153072,  0.1919459 , -0.05143998],\n",
              "       [ 0.93849094,  2.23933883,  1.92563026,  1.65240385,  1.29885946],\n",
              "       [-0.7329189 , -1.49863445,  0.47275953, -1.443767  , -1.40173942],\n",
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              "       [ 1.43991389,  0.18948252, -0.01153072,  0.54245581,  0.75873969],\n",
              "       [ 0.5564544 , -0.05167705, -0.73796609,  0.71771076,  0.89376963],\n",
              "       [-0.37475965,  0.18948252, -1.9486917 ,  0.07510927, -0.32149987],\n",
              "       [ 0.15054059, -0.53399618, -0.01153072,  0.36720086,  0.35364985],\n",
              "       [ 1.67868672,  0.43064208,  0.95704977,  0.89296572,  1.43388941],\n",
              "       [-0.5896552 , -0.4134164 , -1.70654658,  0.07510927,  0.08358997],\n",
              "       [-1.7357648 , -0.53399618,  2.16777538, -1.26851205, -1.13167953],\n",
              "       [ 0.36543614, -1.01631531, -1.70654658, -0.33381896, -0.32149987],\n",
              "       [ 0.79522724,  0.67180165, -0.73796609,  0.8345474 ,  0.89376963],\n",
              "       [-0.68516433, -1.01631531,  0.71490465, -1.443767  , -1.40173942],\n",
              "       [-1.0194463 , -1.01631531,  0.47275953, -1.56060364, -1.40173942],\n",
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              "       [-1.04332358, -1.1368951 ,  0.2306144 , -1.38534869, -1.53676936],\n",
              "       [ 0.86685909, -0.05167705, -0.49582097,  0.71771076,  1.56891935],\n",
              "       [-1.68801023, -1.01631531,  0.95704977, -1.38534869, -1.40173942],\n",
              "       [-0.82842803, -1.01631531,  1.19919489, -1.32693037, -0.86161964],\n",
              "       [-0.18374138,  0.06890273,  0.47275953,  0.54245581,  0.75873969],\n",
              "       [ 0.48482255, -0.89573553, -1.22225633, -0.50907391, -0.18646992],\n",
              "       [ 1.01012279,  1.27470056,  0.47275953,  1.06822067,  1.43388941],\n",
              "       [ 0.1266633 ,  0.18948252, -0.73796609,  0.71771076,  0.4886798 ],\n",
              "       [ 1.34440475,  0.3100623 , -0.98011121,  1.00980236,  0.21861991],\n",
              "       [ 1.36828204,  2.23933883, -0.01153072,  1.30189395,  1.43388941],\n",
              "       [ 0.269927  , -0.4134164 , -1.22225633,  0.07510927,  0.08358997],\n",
              "       [-1.66413295, -1.73979401, -0.25367584, -1.443767  , -1.40173942],\n",
              "       [-1.54474653, -1.8603738 , -0.01153072, -1.61902196, -1.53676936],\n",
              "       [ 0.9862455 ,  0.18948252, -1.9486917 ,  0.65929245,  0.35364985],\n",
              "       [ 1.1295092 ,  1.63643991,  0.47275953,  1.24347563,  0.75873969],\n",
              "       [-1.78351937, -1.49863445,  0.2306144 , -1.38534869, -1.40173942],\n",
              "       [-1.4492374 , -0.89573553,  1.19919489, -1.443767  , -1.26670948],\n",
              "       [-0.94781445, -1.73979401, -0.01153072, -1.50218532, -1.40173942],\n",
              "       [-0.13598681,  0.55122187, -1.22225633,  0.60087413,  0.35364985],\n",
              "       [ 1.39215932,  0.55122187,  0.95704977,  1.00980236,  1.56891935],\n",
              "       [-1.71188752, -1.49863445,  0.95704977, -1.443767  , -1.26670948],\n",
              "       [ 0.81910452,  1.15412078, -0.01153072,  0.95138404,  1.16382952],\n",
              "       [ 0.53257712,  0.55122187,  0.71490465,  1.24347563,  1.7039493 ],\n",
              "       [-1.80739665, -1.37805466,  0.47275953, -1.50218532, -1.40173942],\n",
              "       [-0.35088236,  0.3100623 , -0.25367584,  0.48403749,  0.21861991],\n",
              "       [-0.56577791,  0.79238143, -0.49582097,  0.42561917,  0.35364985],\n",
              "       [ 1.15338649,  0.43064208, -0.49582097,  0.54245581,  0.75873969],\n",
              "       [-0.66128705,  1.39528035,  0.47275953,  0.48403749,  0.21861991],\n",
              "       [ 0.89073637,  0.67180165,  0.47275953,  0.8345474 ,  1.43388941],\n",
              "       [-0.75679618, -0.89573553,  1.92563026, -1.32693037, -1.40173942],\n",
              "       [ 1.46379117,  1.27470056,  0.2306144 ,  0.89296572,  1.16382952],\n",
              "       [-0.39863693,  0.06890273, -0.01153072,  0.1919459 ,  0.35364985],\n",
              "       [ 1.65480944,  0.79238143, -0.01153072,  0.77612908,  1.02879957],\n",
              "       [ 0.03115417, -0.17225683, -0.98011121, -0.21698232, -0.32149987],\n",
              "       [-0.4463915 , -0.77515575, -0.73796609,  0.01669095,  0.21861991],\n",
              "       [ 0.31768157,  0.3100623 , -0.01153072,  0.42561917,  0.21861991],\n",
              "       [-0.8761826 , -1.61921423, -1.70654658, -1.50218532, -1.26670948],\n",
              "       [-0.47026878,  0.91296121, -0.25367584,  0.42561917,  0.08358997],\n",
              "       [ 0.29380429, -0.4134164 , -0.98011121,  0.30878254, -0.05143998],\n",
              "       [-0.70904161, -0.65457597,  1.68348514, -1.38534869, -1.40173942],\n",
              "       [ 0.24604972, -0.29283662, -0.01153072,  0.13352758,  0.08358997],\n",
              "       [ 0.6997181 ,  1.7570197 , -0.25367584,  1.41873058,  0.75873969],\n",
              "       [ 1.10563192,  1.033541  ,  0.71490465,  1.06822067,  1.16382952],\n",
              "       [-1.35372827, -0.89573553,  1.68348514, -1.38534869, -1.13167953],\n",
              "       [-0.49414606, -1.1368951 , -1.46440146, -0.33381896, -0.32149987],\n",
              "       [ 1.58317759,  1.033541  ,  0.71490465,  1.06822067,  1.7039493 ],\n",
              "       [ 1.22501834,  1.63643991, -0.01153072,  1.12663899,  0.4886798 ],\n",
              "       [-0.97169173, -1.1368951 ,  0.2306144 , -1.38534869, -1.53676936],\n",
              "       [ 1.48766846,  1.033541  ,  0.2306144 ,  1.00980236,  1.56891935],\n",
              "       [-1.83127393, -1.1368951 , -0.01153072, -1.443767  , -1.40173942],\n",
              "       [-0.61353248,  1.27470056,  0.2306144 ,  0.60087413,  0.35364985],\n",
              "       [ 1.24889562,  1.87759948, -0.49582097,  1.30189395,  0.89376963],\n",
              "       [ 0.60420897,  0.55122187, -0.25367584,  1.00980236,  0.75873969],\n",
              "       [ 0.50869984, -0.17225683, -0.49582097,  0.13352758,  0.08358997],\n",
              "       [ 0.91461365,  0.79238143, -0.01153072,  0.95138404,  0.75873969],\n",
              "       [ 0.22217244,  0.55122187, -1.70654658,  0.30878254,  0.08358997],\n",
              "       [-0.08823225,  0.67180165, -0.25367584,  0.25036422,  0.08358997],\n",
              "       [ 1.03400007, -0.29283662, -0.49582097,  0.60087413,  1.02879957],\n",
              "       [ 1.70256401,  0.06890273, -0.01153072,  0.71771076,  0.75873969],\n",
              "       [-1.37760555, -0.53399618,  0.95704977, -1.26851205, -1.40173942],\n",
              "       [-0.1598641 ,  0.3100623 , -0.49582097,  0.07510927,  0.08358997],\n",
              "       [ 0.67584082, -1.1368951 , -1.22225633,  0.36720086,  0.62370974],\n",
              "       [-1.52086925, -0.05167705,  2.40992051, -1.56060364, -1.40173942],\n",
              "       [ 0.34155885, -0.05167705, -0.98011121,  0.07510927, -0.05143998],\n",
              "       [ 0.58033169,  1.51586013, -0.01153072,  1.18505731,  1.16382952]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXzSZUyGUVqj",
        "outputId": "b094a26c-c151-492a-b38a-8ba3336e0272"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.11210953,  0.3100623 , -0.49582097,  0.48403749, -0.05143998],\n",
              "       [-1.42536012, -0.17225683,  1.92563026, -1.26851205, -1.26670948],\n",
              "       [ 0.96236822,  2.23933883, -0.98011121,  1.76924049,  1.43388941],\n",
              "       [ 0.00727689,  0.18948252, -0.25367584,  0.36720086,  0.35364985],\n",
              "       [-0.04047768,  1.15412078, -0.49582097,  0.54245581,  0.21861991],\n",
              "       [-1.11495543, -0.53399618,  0.95704977, -1.38534869, -1.13167953],\n",
              "       [-0.32700508, -0.29283662, -0.25367584, -0.15856401,  0.08358997],\n",
              "       [ 1.51154574,  1.27470056,  0.2306144 ,  0.71771076,  1.43388941],\n",
              "       [-0.23149595,  0.43064208, -1.9486917 ,  0.36720086,  0.35364985],\n",
              "       [ 0.10278602, -0.05167705, -0.73796609,  0.01669095, -0.05143998],\n",
              "       [ 0.77134995,  0.79238143,  0.47275953,  0.71771076,  1.02879957],\n",
              "       [-1.56862382, -1.25747488, -0.01153072, -1.443767  , -1.53676936],\n",
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              "       [-0.51802335,  0.55122187,  0.71490465,  0.48403749,  0.4886798 ],\n",
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              "       [ 1.20114105,  0.67180165, -0.49582097,  1.00980236,  1.16382952],\n",
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              "       [-1.49699197, -0.17225683,  3.378501  , -1.38534869, -1.13167953],\n",
              "       [-0.3031278 ,  1.033541  ,  0.2306144 ,  0.30878254,  0.21861991],\n",
              "       [-1.5925011 , -1.25747488,  0.95704977, -1.32693037, -1.40173942],\n",
              "       [-0.85230531, -1.73979401,  0.47275953, -1.50218532, -1.40173942],\n",
              "       [ 1.63093216,  0.55122187, -1.22225633,  0.65929245,  0.89376963],\n",
              "       [-0.63740976,  0.67180165,  0.47275953,  0.36720086,  0.35364985],\n",
              "       [-1.21046457, -0.77515575,  1.19919489, -1.38534869, -1.40173942],\n",
              "       [-1.75964208, -1.01631531,  1.44134002, -1.443767  , -1.40173942],\n",
              "       [-1.09107815, -0.77515575,  2.65206563, -1.38534869, -1.53676936],\n",
              "       [ 1.53542302, -0.05167705, -0.73796609,  0.71771076,  0.89376963],\n",
              "       [ 0.17441787,  0.18948252,  0.95704977,  0.36720086,  0.4886798 ],\n",
              "       [ 0.19829515,  1.033541  ,  0.2306144 ,  0.48403749,  0.35364985],\n",
              "       [-1.47311468, -0.53399618,  2.16777538, -1.50218532, -1.13167953],\n",
              "       [-1.61637838, -0.53399618,  1.68348514, -1.38534869, -1.40173942]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Creation\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn=KNeighborsClassifier(n_neighbors=7)\n",
        "knn.fit(x_train,y_train) #model created"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "6E6zCDSeUldR",
        "outputId": "9b31d857-a330-4041-ff2c-b99c925d4ce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=knn.predict(x_test)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9GVgSyjbUn-p",
        "outputId": "f8a458fe-1e07-4ca3-e8b8-bdda0a2d6fd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSkRsQNXUrM0",
        "outputId": "201c44b1-f46e-48be-8d89-f1f6418436d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-versicolor', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-setosa',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-virginica', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica',\n",
              "       'Iris-setosa', 'Iris-virginica', 'Iris-setosa', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-virginica', 'Iris-virginica',\n",
              "       'Iris-virginica', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-virginica', 'Iris-versicolor', 'Iris-setosa', 'Iris-setosa',\n",
              "       'Iris-setosa', 'Iris-virginica', 'Iris-versicolor',\n",
              "       'Iris-versicolor', 'Iris-setosa', 'Iris-setosa'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Performance Evaluation\n",
        "\n",
        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "cm=confusion_matrix(y_test,y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXBJbP8yUuhp",
        "outputId": "1980b667-f1c4-40be-b62c-933f0f833edb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19,  0,  0],\n",
              "       [ 0, 13,  0],\n",
              "       [ 0,  0, 13]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score=accuracy_score(y_test,y_pred)\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usCMDIF1U1XQ",
        "outputId": "969a6636-6160-48a6-81b8-4da8173e04b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "lab=['Iris-versicolor','Iris-virginica','Iris-setosa']\n",
        "cmd=ConfusionMatrixDisplay(cm,display_labels=lab)\n",
        "cmd.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "nVaYSGfKU2g9",
        "outputId": "f162ef92-8eea-4c11-88fe-fa9120f0e652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f2260e99d50>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "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": [
        "with open('model.pkl', 'wb') as file:\n",
        "    pickle.dump(knn, file)"
      ],
      "metadata": {
        "id": "OZWSLvfTU5L_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}