CODSOFT_task_3_Classification_of_IRIS_flower.ipynb

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        "Import Necessary Libraries."
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as snr\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load data and show first 10 rows of data."
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      "source": [
        "data=pd.read_csv(\"/content/IRIS.csv\")\n",
        "data.head(10)"
      ],
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              "   sno  sepal_length  sepal_width  petal_length  petal_width      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\n",
              "5    6           5.4          3.9           1.7          0.4  Iris-setosa\n",
              "6    7           4.6          3.4           1.4          0.3  Iris-setosa\n",
              "7    8           5.0          3.4           1.5          0.2  Iris-setosa\n",
              "8    9           4.4          2.9           1.4          0.2  Iris-setosa\n",
              "9   10           4.9          3.1           1.5          0.1  Iris-setosa"
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        "Calculates statistical values and rounds them to 3 decimal places."
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        "data.describe().round(3)"
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              "           sno  sepal_length  sepal_width  petal_length  petal_width\n",
              "count  150.000       150.000      150.000       150.000      150.000\n",
              "mean    75.500         5.843        3.054         3.759        1.199\n",
              "std     43.445         0.828        0.434         1.764        0.763\n",
              "min      1.000         4.300        2.000         1.000        0.100\n",
              "25%     38.250         5.100        2.800         1.600        0.300\n",
              "50%     75.500         5.800        3.000         4.350        1.300\n",
              "75%    112.750         6.400        3.300         5.100        1.800\n",
              "max    150.000         7.900        4.400         6.900        2.500"
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              "      <td>150.000</td>\n",
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              "      <td>150.000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>75.500</td>\n",
              "      <td>5.843</td>\n",
              "      <td>3.054</td>\n",
              "      <td>3.759</td>\n",
              "      <td>1.199</td>\n",
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              "      <th>std</th>\n",
              "      <td>43.445</td>\n",
              "      <td>0.828</td>\n",
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              "      <td>0.763</td>\n",
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              "      <td>1.000</td>\n",
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              "      <td>1.000</td>\n",
              "      <td>0.100</td>\n",
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              "      <th>25%</th>\n",
              "      <td>38.250</td>\n",
              "      <td>5.100</td>\n",
              "      <td>2.800</td>\n",
              "      <td>1.600</td>\n",
              "      <td>0.300</td>\n",
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              "      <th>50%</th>\n",
              "      <td>75.500</td>\n",
              "      <td>5.800</td>\n",
              "      <td>3.000</td>\n",
              "      <td>4.350</td>\n",
              "      <td>1.300</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750</td>\n",
              "      <td>6.400</td>\n",
              "      <td>3.300</td>\n",
              "      <td>5.100</td>\n",
              "      <td>1.800</td>\n",
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              "      <th>max</th>\n",
              "      <td>150.000</td>\n",
              "      <td>7.900</td>\n",
              "      <td>4.400</td>\n",
              "      <td>6.900</td>\n",
              "      <td>2.500</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "        document.querySelector('#df-90011b19-3cf5-435c-ab79-e2ef88a871a7 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-90011b19-3cf5-435c-ab79-e2ef88a871a7');\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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              "  </div>\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-603f633e-21e1-4f5a-aa3c-a7c747707b8d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\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",
              "      --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",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      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-603f633e-21e1-4f5a-aa3c-a7c747707b8d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 720
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mark null values as True and returns sum of number of True values in each column."
      ],
      "metadata": {
        "id": "_Z8BxpoR55ht"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_iPXMbxv53HG",
        "outputId": "f869525c-dd0f-41ca-bdb8-d3d5a6615295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sno             0\n",
              "sepal_length    0\n",
              "sepal_width     0\n",
              "petal_length    0\n",
              "petal_width     0\n",
              "species         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 721
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Extracting all information about data"
      ],
      "metadata": {
        "id": "aC8AT8CW6Hew"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfZ0JvAZ6FGF",
        "outputId": "13b57aa2-1a98-4ad0-db52-1c95436ce222"
      },
      "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   sno           150 non-null    int64  \n",
            " 1   sepal_length  150 non-null    float64\n",
            " 2   sepal_width   150 non-null    float64\n",
            " 3   petal_length  150 non-null    float64\n",
            " 4   petal_width   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": "markdown",
      "source": [
        "Shape of data"
      ],
      "metadata": {
        "id": "Jo5JLvFD61Hp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1yLw7d76kPE",
        "outputId": "2fcd4d3a-85e5-4e48-e8a5-ce2ef236456e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 723
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking Unique values of species column."
      ],
      "metadata": {
        "id": "6bW3QXA966O7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['species'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bNG_1yOS64fT",
        "outputId": "222875c2-3f38-4386-cc16-9f41c622db5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 724
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change the catagorical data into numerical data by using OneHotEncoding."
      ],
      "metadata": {
        "id": "fzGwVmWX7_sZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['species']=data['species'].apply({'Iris-setosa':0,'Iris-versicolor':1,'Iris-virginica':2}.get)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3KA8jUdMAZtv",
        "outputId": "bc05f201-d12c-4944-8f24-d272e4b75e48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sno  sepal_length  sepal_width  petal_length  petal_width  species\n",
              "0    1           5.1          3.5           1.4          0.2        0\n",
              "1    2           4.9          3.0           1.4          0.2        0\n",
              "2    3           4.7          3.2           1.3          0.2        0\n",
              "3    4           4.6          3.1           1.5          0.2        0\n",
              "4    5           5.0          3.6           1.4          0.2        0"
            ],
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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",
              "      --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",
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              "\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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-ad42b0f4-39aa-41ff-967f-1aa1f94b64a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 725
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deviding the data into Dependent and Independent variables."
      ],
      "metadata": {
        "id": "6ym-lnwuBTcg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data.drop(['species'],axis=1)\n",
        "y=data['species']\n",
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Z_KmbjZk8jAL",
        "outputId": "3a7a1dfa-40ab-46ea-dcb7-0438c1337f4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sno  sepal_length  sepal_width  petal_length  petal_width\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"
            ],
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              "      <th>sno</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>1</td>\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>2</td>\n",
              "      <td>4.9</td>\n",
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              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-b878f17e-88e0-4c75-9894-32fb9d10d8f4 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-b878f17e-88e0-4c75-9894-32fb9d10d8f4');\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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              "        element.appendChild(docLink);\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    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",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    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-3570a5e6-efcf-47ab-aa0d-cb49852bfeee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 726
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yqexajn_s4p",
        "outputId": "dbb3f317-fecd-428d-e696-d041edfc9a5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 727
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deviding the cleaned data into training and testing sets."
      ],
      "metadata": {
        "id": "WN8mdLLnBSdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.8)"
      ],
      "metadata": {
        "id": "0r_KL-wpBMqI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating first machine learning model 'Logistic Regression'."
      ],
      "metadata": {
        "id": "H71CJjqQLCSs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "log=LogisticRegression()"
      ],
      "metadata": {
        "id": "vcpVSwf5CJKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model."
      ],
      "metadata": {
        "id": "26XpRi85MyUk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "B8Ot2plGMnij",
        "outputId": "a82e2ace-6fcc-4edf-e7f7-2e92d3311d87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-67 {color: black;background-color: white;}#sk-container-id-67 pre{padding: 0;}#sk-container-id-67 div.sk-toggleable {background-color: white;}#sk-container-id-67 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-67 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-67 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-67 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-67 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-67 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-67 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-67 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-67 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-67 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-67 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-67 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-67 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-67 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-67 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-67 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-67 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-67 div.sk-item {position: relative;z-index: 1;}#sk-container-id-67 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-67 div.sk-item::before, #sk-container-id-67 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-67 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-67 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-67 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-67 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-67 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-67 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-67 div.sk-label-container {text-align: center;}#sk-container-id-67 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-67 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-67\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-67\" type=\"checkbox\" checked><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 730
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model"
      ],
      "metadata": {
        "id": "MpWC56-WlnMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=log.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ag5Yrq9BM-1R",
        "outputId": "332eac31-d3bc-48b6-f35b-5a0fb1cd610c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 2 2 2 1 0 2 2 2 1 2 1 0 2 1 1 0 2 0 2 2 0 0 1 1 2 2 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check confusion metrics and accuracy score."
      ],
      "metadata": {
        "id": "SZ0_K6RzOeUN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "ac=accuracy_score(y_test,predictions)"
      ],
      "metadata": {
        "id": "KpZ1296eOBi_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TA7TT-_RPQ-u",
        "outputId": "4dffc53d-1bbd-45b6-88c1-d5e5713c28a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 7  0  0]\n",
            " [ 0  8  0]\n",
            " [ 0  1 14]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-cLGgFHPTa_",
        "outputId": "1feea814-74ae-4261-cfbf-6f8c62426549"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating second machine learning model 'Random Forest'."
      ],
      "metadata": {
        "id": "0Fe44LyHPcF9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "forest=RandomForestClassifier()"
      ],
      "metadata": {
        "id": "_6QhU-GMPVb1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model."
      ],
      "metadata": {
        "id": "6EMtF4RiP0ry"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "forest.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "x-Wl4JhRPzCP",
        "outputId": "a32297fa-1f92-428c-f669-6d2f20af44ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-68 {color: black;background-color: white;}#sk-container-id-68 pre{padding: 0;}#sk-container-id-68 div.sk-toggleable {background-color: white;}#sk-container-id-68 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-68 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-68 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-68 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-68 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-68 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-68 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-68 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-68 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-68 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-68 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-68 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-68 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-68 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-68 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-68 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-68 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-68 div.sk-item {position: relative;z-index: 1;}#sk-container-id-68 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-68 div.sk-item::before, #sk-container-id-68 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-68 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-68 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-68 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-68 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-68 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-68 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-68 div.sk-label-container {text-align: center;}#sk-container-id-68 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-68 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-68\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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-68\" type=\"checkbox\" checked><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 736
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model."
      ],
      "metadata": {
        "id": "exsbthmpQDcD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=forest.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8UHhQWZnQB08",
        "outputId": "e58dbaeb-949c-4f70-8187-18796cb3726d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 2 2 2 1 0 2 2 2 2 2 1 0 2 1 1 0 2 0 2 2 0 0 1 1 2 2 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check cofusion metrics and accuracy score."
      ],
      "metadata": {
        "id": "QLjs7APHQcZ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "ac=accuracy_score(y_test,predictions)"
      ],
      "metadata": {
        "id": "eqxy_V6KQNJQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T36zhQuCQY3M",
        "outputId": "512ab870-8498-4622-8e9e-bc3e8b1fa2f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 7  0  0]\n",
            " [ 0  8  0]\n",
            " [ 0  0 15]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9HqozMlQa3M",
        "outputId": "bf31ec6c-06be-4720-b56b-6084da262cd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating third machine learning model."
      ],
      "metadata": {
        "id": "idM7nKcKQnUU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm=SVC()"
      ],
      "metadata": {
        "id": "M8IM3YSFQlnb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model."
      ],
      "metadata": {
        "id": "7n4OQ8rJQ9XE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svm.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "d7R_gtQ_Q7We",
        "outputId": "79e2d1f5-d216-4337-dcd3-6c7a97b885ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-69 {color: black;background-color: white;}#sk-container-id-69 pre{padding: 0;}#sk-container-id-69 div.sk-toggleable {background-color: white;}#sk-container-id-69 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-69 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-69 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-69 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-69 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-69 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-69 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-69 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-69 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-69 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-69 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-69 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-69 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-69 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-69 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-69 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-69 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-69 div.sk-item {position: relative;z-index: 1;}#sk-container-id-69 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-69 div.sk-item::before, #sk-container-id-69 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-69 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-69 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-69 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-69 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-69 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-69 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-69 div.sk-label-container {text-align: center;}#sk-container-id-69 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-69 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-69\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</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-69\" type=\"checkbox\" checked><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 742
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model."
      ],
      "metadata": {
        "id": "yMidLNUMRHYG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=svm.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l6b8N9mRF0_",
        "outputId": "1cb638d7-813d-4ac8-9fdc-a32fafb32359"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 2 2 2 1 0 2 2 2 1 2 1 0 2 1 1 0 2 0 2 2 0 0 1 1 2 2 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check confusion metrics ad accuracy score."
      ],
      "metadata": {
        "id": "S02wizkKRbW8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm=confusion_matrix(y_test,predictions)\n",
        "ac=accuracy_score(y_test,predictions)"
      ],
      "metadata": {
        "id": "8qYd0AYWRUgl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h42BzMdLRlO3",
        "outputId": "9c43bcb3-06b1-4cba-ef3d-d7147810022d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 7  0  0]\n",
            " [ 0  8  0]\n",
            " [ 0  1 14]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZbJ3KfERnWX",
        "outputId": "a5bd2b20-a810-4e07-9e8e-b681b229ab37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "snr.scatterplot(x='sno', y='sepal_length', hue='species', palette='deep', data=data)\n",
        "plt.xlabel('Count')\n",
        "plt.title('Sepal length of various species of Iris flower')\n",
        "plt.legend(title=\"Lenends\", labels=['Setosa','Versicolor','Virginica'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "IJehyCZLdCOK",
        "outputId": "7ed09aa1-a912-4c03-da1f-1704a0de0a8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "snr.scatterplot(x='sno', y='sepal_width', hue='species', palette='deep', data=data)\n",
        "plt.xlabel('Count')\n",
        "plt.title('Sepal length of various species of Iris flower')\n",
        "plt.legend(title=\"Lenends\", labels=['Setosa','Versicolor','Virginica'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ACReQ6WqeBCl",
        "outputId": "f9464543-db00-43be-dcb2-4de834d13726"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "snr.scatterplot(x='sno', y='petal_length', hue='species', palette='deep', data=data)\n",
        "plt.xlabel('Count')\n",
        "plt.title('Sepal length of various species of Iris flower')\n",
        "plt.legend(title=\"Lenends\", labels=['Setosa','Versicolor','Virginica'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "QJmwTfCjNMSR",
        "outputId": "f6138fc3-6928-4a88-ea82-b1ffdea1dc51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "snr.scatterplot(x='sno', y='petal_width', hue='species', palette='deep', data=data)\n",
        "plt.xlabel('Count')\n",
        "plt.title('Sepal length of various species of Iris flower')\n",
        "plt.legend(title=\"Lenends\", labels=['Setosa','Versicolor','Virginica'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zpf3ds2QNszT",
        "outputId": "3c0e7052-8f53-421c-c34d-b94583440200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}