iris_flower_dataset_classification_ (1).ipynb

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      "source": [
        "**Project Overview**: this project is all about claassify the flowers species('Iris-setosa' 'Iris-versicolor' 'Iris-virginica'), based on their characterstics (sepal and petal width and length), so we are going to bulid a neural network using tensorflow to classify these flowers.\n"
      ],
      "metadata": {
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      "source": [
        "## importing the required libraries\n"
      ],
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        "id": "DqVWuTROuFW5"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##EDA\n"
      ],
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        "id": "YL_S3xUMxLD4"
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      "source": [
        "# reading the dataset and stores it into dataframe df, then presentinf the first 5 rows from the dataframe\n",
        "df = pd.read_csv('/content/IRIS.csv')\n",
        "df.head()"
      ],
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              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
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              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
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              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
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    {
      "cell_type": "code",
      "source": [
        "#count the occurencies for each species class\n",
        "df['species'].value_counts()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfdRkwjOwX9s",
        "outputId": "e60f06b3-d33d-4d54-f203-dca1d3a01471"
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      "execution_count": null,
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          "data": {
            "text/plain": [
              "species\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "#presenting a summary for the dataset and its fields\n",
        "df.info()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "uOUrfothwogN",
        "outputId": "cb974e5a-1196-4582-ed03-e7061f14534c"
      },
      "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 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   sepal_length  150 non-null    float64\n",
            " 1   sepal_width   150 non-null    float64\n",
            " 2   petal_length  150 non-null    float64\n",
            " 3   petal_width   150 non-null    float64\n",
            " 4   species       150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "#checking for null values\n",
        "df.isnull().sum()"
      ],
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        },
        "id": "802Kj24qxPXE",
        "outputId": "9ff90e00-6282-45a5-e964-4fedddfaef3a"
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length    0\n",
              "sepal_width     0\n",
              "petal_length    0\n",
              "petal_width     0\n",
              "species         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "qAxNRH7b0i4q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#converting species column from species names to numerical values\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "df['species'] = le.fit_transform(df['species'])\n",
        "df.head()\n"
      ],
      "metadata": {
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      "execution_count": null,
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            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width  species\n",
              "0           5.1          3.5           1.4          0.2        0\n",
              "1           4.9          3.0           1.4          0.2        0\n",
              "2           4.7          3.2           1.3          0.2        0\n",
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              "4           5.0          3.6           1.4          0.2        0"
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              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7ebf44f-19ce-4ca7-b562-7d5d0cda3902')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-a7ebf44f-19ce-4ca7-b562-7d5d0cda3902 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-a7ebf44f-19ce-4ca7-b562-7d5d0cda3902');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --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-5741b76f-beb4-4266-b511-83a64b31d538 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print the original species names\n",
        "species_name = le.classes_\n",
        "print(species_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sc6lkHFYyrLL",
        "outputId": "c5e25479-5c9e-42ed-fc04-8bf55813887f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating new dataframe from the original one, x dataframe represents the input\n",
        "# features , y represents the target variable for the classification task which\n",
        "# the model will try to predict.\n",
        "X = df.drop(columns=['species'])\n",
        "y = df['species']\n",
        "X.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "u-kYnh5L0c4v",
        "outputId": "81e41d87-7337-48b2-ec48-186da920966e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width\n",
              "0           5.1          3.5           1.4          0.2\n",
              "1           4.9          3.0           1.4          0.2\n",
              "2           4.7          3.2           1.3          0.2"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2b1ce7f8-a31a-485c-b25b-6c57e8a01e3c 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-2b1ce7f8-a31a-485c-b25b-6c57e8a01e3c');\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",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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-61078a9d-d68a-4ca8-b832-e8d528c969ac 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",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#representing the first 5 rows of the target df that contains species coulmn\n",
        "print(y[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6-8_PlO5gAs",
        "outputId": "955ee10e-ab66-43dd-9bb8-faa3c29ed1f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    0\n",
            "1    0\n",
            "2    0\n",
            "3    0\n",
            "4    0\n",
            "Name: species, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##splitting data into train and test\n",
        "\n",
        "shuffle is set to the defult value 'true' to randomize tha dataset and avoid an bias specill if it related to the order of the data, setting the random state = 69"
      ],
      "metadata": {
        "id": "-niG2Hcf6HT7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#\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=0.3, shuffle=True, random_state=69)"
      ],
      "metadata": {
        "id": "mprS9bM75n92"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#showing number of rows and columns\n",
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zyj6BsFD6sut",
        "outputId": "668a8daf-33cf-4eb8-e5d0-baf07d7196c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(105, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#standrize the data for each field  by using standardsacalaer setting the mean to 0 and standard deviation to 1\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "print(x_train[:1])\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)\n",
        "print(x_train[:1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avSaRLSf6wuf",
        "outputId": "733ece26-ca27-4c00-e717-7221290fe412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    sepal_length  sepal_width  petal_length  petal_width\n",
            "57           4.9          2.4           3.3          1.0\n",
            "[[-1.14630942 -1.44374444 -0.25650655 -0.258263  ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# applying one hot encoding for the species classes dataframe y, each column\n",
        "# represent a class and each row representing the samples incase of this sample\n",
        "# from the column category the value for it is set to 1 else it set to 0\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes=3)\n",
        "print(y_train[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9bVNqEpe9Qjw",
        "outputId": "08252761-012e-45a8-bcf8-8e9e02530675"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 1. 0.]\n",
            " [0. 0. 1.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]\n",
            " [0. 1. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##intializing sequential model, and the required layers"
      ],
      "metadata": {
        "id": "qjmAvfMY_ySV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout"
      ],
      "metadata": {
        "id": "yh80PQHV_bkz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units=32, activation='relu', input_shape=(x_train.shape[-1], )))\n",
        "model.add(Dense(units=32, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(units=3, activation='softmax'))"
      ],
      "metadata": {
        "id": "5LNQLGLa_j2g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# setting the adam optimizier, loss function and accuracy then geeting the data\n",
        "# summary\n",
        "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdwDQr9__5t_",
        "outputId": "b46ed855-a9ca-4e3d-9b09-e58e19179ce5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_3 (Dense)             (None, 32)                160       \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 3)                 99        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1315 (5.14 KB)\n",
            "Trainable params: 1315 (5.14 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#training the model, through 100 epoch getting the loss and accuracy for each epoch\n",
        "new_var = model.fit(x_train, y_train, epochs=100)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPpi_zB4ABbS",
        "outputId": "c8a0028b-232e-41d9-b599-2af9ed87edeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.1460 - accuracy: 0.9333\n",
            "Epoch 2/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.1182 - accuracy: 0.9619\n",
            "Epoch 3/100\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 0.0948 - accuracy: 0.9619\n",
            "Epoch 4/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.1137 - accuracy: 0.9619\n",
            "Epoch 5/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0962 - accuracy: 0.9714\n",
            "Epoch 6/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.1135 - accuracy: 0.9524\n",
            "Epoch 7/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.1706 - accuracy: 0.9524\n",
            "Epoch 8/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.0951 - accuracy: 0.9714\n",
            "Epoch 9/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0927 - accuracy: 0.9619\n",
            "Epoch 10/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0891 - accuracy: 0.9619\n",
            "Epoch 11/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.1085 - accuracy: 0.9714\n",
            "Epoch 12/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0931 - accuracy: 0.9619\n",
            "Epoch 13/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.1018 - accuracy: 0.9619\n",
            "Epoch 14/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1098 - accuracy: 0.9524\n",
            "Epoch 15/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0958 - accuracy: 0.9524\n",
            "Epoch 16/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.1298 - accuracy: 0.9524\n",
            "Epoch 17/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.1340 - accuracy: 0.9619\n",
            "Epoch 18/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.1126 - accuracy: 0.9810\n",
            "Epoch 19/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0974 - accuracy: 0.9524\n",
            "Epoch 20/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0734 - accuracy: 0.9714\n",
            "Epoch 21/100\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 0.0869 - accuracy: 0.9714\n",
            "Epoch 22/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0829 - accuracy: 0.9714\n",
            "Epoch 23/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1056 - accuracy: 0.9619\n",
            "Epoch 24/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1376 - accuracy: 0.9524\n",
            "Epoch 25/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0921 - accuracy: 0.9714\n",
            "Epoch 26/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.1158 - accuracy: 0.9619\n",
            "Epoch 27/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.1532 - accuracy: 0.9429\n",
            "Epoch 28/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0850 - accuracy: 0.9714\n",
            "Epoch 29/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0838 - accuracy: 0.9810\n",
            "Epoch 30/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0934 - accuracy: 0.9714\n",
            "Epoch 31/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0745 - accuracy: 0.9810\n",
            "Epoch 32/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0857 - accuracy: 0.9619\n",
            "Epoch 33/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0917 - accuracy: 0.9524\n",
            "Epoch 34/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0868 - accuracy: 0.9810\n",
            "Epoch 35/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0807 - accuracy: 0.9714\n",
            "Epoch 36/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0776 - accuracy: 0.9714\n",
            "Epoch 37/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1177 - accuracy: 0.9429\n",
            "Epoch 38/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0724 - accuracy: 0.9810\n",
            "Epoch 39/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.1101 - accuracy: 0.9524\n",
            "Epoch 40/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0982 - accuracy: 0.9714\n",
            "Epoch 41/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0991 - accuracy: 0.9524\n",
            "Epoch 42/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0990 - accuracy: 0.9714\n",
            "Epoch 43/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1046 - accuracy: 0.9714\n",
            "Epoch 44/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0757 - accuracy: 0.9905\n",
            "Epoch 45/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1212 - accuracy: 0.9619\n",
            "Epoch 46/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0694 - accuracy: 0.9810\n",
            "Epoch 47/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0703 - accuracy: 0.9810\n",
            "Epoch 48/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0825 - accuracy: 0.9905\n",
            "Epoch 49/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0733 - accuracy: 0.9810\n",
            "Epoch 50/100\n",
            "4/4 [==============================] - 0s 5ms/step - loss: 0.0645 - accuracy: 0.9905\n",
            "Epoch 51/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0689 - accuracy: 0.9810\n",
            "Epoch 52/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.1148 - accuracy: 0.9524\n",
            "Epoch 53/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0857 - accuracy: 0.9810\n",
            "Epoch 54/100\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 0.0967 - accuracy: 0.9714\n",
            "Epoch 55/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0908 - accuracy: 0.9810\n",
            "Epoch 56/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0658 - accuracy: 0.9714\n",
            "Epoch 57/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0767 - accuracy: 0.9619\n",
            "Epoch 58/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0947 - accuracy: 0.9524\n",
            "Epoch 59/100\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 0.0975 - accuracy: 0.9714\n",
            "Epoch 60/100\n",
            "4/4 [==============================] - 0s 15ms/step - loss: 0.1121 - accuracy: 0.9524\n",
            "Epoch 61/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0923 - accuracy: 0.9810\n",
            "Epoch 62/100\n",
            "4/4 [==============================] - 0s 12ms/step - loss: 0.1080 - accuracy: 0.9714\n",
            "Epoch 63/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0750 - accuracy: 0.9810\n",
            "Epoch 64/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0811 - accuracy: 0.9619\n",
            "Epoch 65/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0826 - accuracy: 0.9714\n",
            "Epoch 66/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0875 - accuracy: 0.9619\n",
            "Epoch 67/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0932 - accuracy: 0.9810\n",
            "Epoch 68/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0838 - accuracy: 0.9905\n",
            "Epoch 69/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0767 - accuracy: 0.9810\n",
            "Epoch 70/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0712 - accuracy: 0.9810\n",
            "Epoch 71/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0883 - accuracy: 0.9714\n",
            "Epoch 72/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0813 - accuracy: 0.9714\n",
            "Epoch 73/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.0780 - accuracy: 0.9810\n",
            "Epoch 74/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0649 - accuracy: 0.9905\n",
            "Epoch 75/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0968 - accuracy: 0.9619\n",
            "Epoch 76/100\n",
            "4/4 [==============================] - 0s 14ms/step - loss: 0.0513 - accuracy: 0.9810\n",
            "Epoch 77/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0723 - accuracy: 0.9905\n",
            "Epoch 78/100\n",
            "4/4 [==============================] - 0s 10ms/step - loss: 0.0542 - accuracy: 0.9714\n",
            "Epoch 79/100\n",
            "4/4 [==============================] - 0s 8ms/step - loss: 0.1223 - accuracy: 0.9714\n",
            "Epoch 80/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0980 - accuracy: 0.9714\n",
            "Epoch 81/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0810 - accuracy: 0.9905\n",
            "Epoch 82/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0876 - accuracy: 0.9619\n",
            "Epoch 83/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0523 - accuracy: 0.9810\n",
            "Epoch 84/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0527 - accuracy: 0.9714\n",
            "Epoch 85/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0589 - accuracy: 0.9810\n",
            "Epoch 86/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0789 - accuracy: 0.9714\n",
            "Epoch 87/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.1079 - accuracy: 0.9619\n",
            "Epoch 88/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0945 - accuracy: 0.9714\n",
            "Epoch 89/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0785 - accuracy: 0.9714\n",
            "Epoch 90/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0688 - accuracy: 0.9714\n",
            "Epoch 91/100\n",
            "4/4 [==============================] - 0s 11ms/step - loss: 0.0787 - accuracy: 0.9810\n",
            "Epoch 92/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0844 - accuracy: 0.9810\n",
            "Epoch 93/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0641 - accuracy: 0.9810\n",
            "Epoch 94/100\n",
            "4/4 [==============================] - 0s 9ms/step - loss: 0.0971 - accuracy: 0.9810\n",
            "Epoch 95/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0800 - accuracy: 0.9714\n",
            "Epoch 96/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0670 - accuracy: 0.9810\n",
            "Epoch 97/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0714 - accuracy: 0.9714\n",
            "Epoch 98/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0677 - accuracy: 0.9810\n",
            "Epoch 99/100\n",
            "4/4 [==============================] - 0s 6ms/step - loss: 0.0510 - accuracy: 0.9905\n",
            "Epoch 100/100\n",
            "4/4 [==============================] - 0s 7ms/step - loss: 0.0691 - accuracy: 0.9810\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#giving predictions on the test data, representing the first five predctions\n",
        "prediction = model.predict(x_test)\n",
        "print(prediction[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YblYMQ9cDBxz",
        "outputId": "cf0dfb56-cc68-48c2-d49c-104e83c90cd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 6ms/step\n",
            "[[9.9959451e-01 1.1668835e-04 2.8887010e-04]\n",
            " [1.0762533e-02 8.7363827e-01 1.1559917e-01]\n",
            " [9.9750066e-01 1.2852958e-03 1.2139981e-03]\n",
            " [2.8399852e-04 6.5980270e-03 9.9311781e-01]\n",
            " [9.9652886e-01 1.7172286e-03 1.7538605e-03]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the predictions into defined lables refering to classes for each\n",
        "# sample, using argmax function with -1 parametr refering to classes\n",
        "prediction = np.argmax(prediction, axis=-1)\n",
        "print(prediction[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWBooOILIgU4",
        "outputId": "037b9c5c-69a0-466d-bdba-fefd04fa9dbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 0 2 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the argmax functions working on converting these probability distributions into class labels by selecting the class with the highest probability."
      ],
      "metadata": {
        "id": "VSQiM-MT2Tq1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#presenting the sample index and the corresponding class label\n",
        "print(y_test[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXzS3AgrIskO",
        "outputId": "f629375e-b299-4830-b918-04b8b6b0362f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4      0\n",
            "58     1\n",
            "20     0\n",
            "114    2\n",
            "31     0\n",
            "Name: species, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#imorting the accuracy_score function from sklearn.metrics module,\n",
        "#this function take the y_test: the actual target labels and predection: the predicted labels generated by the model for the test dataset\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(y_test, prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tc8RsmJ4JJ5i",
        "outputId": "9c33d58a-3214-4358-9844-a46e8a437ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9777777777777777\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This model gives 97% accuracy."
      ],
      "metadata": {
        "id": "70wWIAeq-7_H"
      }
    }
  ]
}