{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dz-Bq449QyKN",
        "outputId": "2d3063c6-322d-485f-dae8-32428ec8c1df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "D1Z3ZmgmRMuR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAYDGopvXl39",
        "outputId": "557d01d1-e2e5-418a-a18d-c1d75f4ab250"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/drive/MyDrive/Machine Learning/Iris.csv'\n",
        "df = pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "tm4dKgJzfYe8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(file_path)\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "7HzDbTv_gmVZ",
        "outputId": "d89809e6-be90-4af1-fea6-8e1ca98df00d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa\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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              "      <td>Iris-setosa</td>\n",
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              "      <th>9</th>\n",
              "      <td>10</td>\n",
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              "      <td>3.1</td>\n",
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              "      <td>Iris-setosa</td>\n",
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              "                                                    [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",
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              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\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\": \"SepalWidthCm\",\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\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\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\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n"
      ],
      "metadata": {
        "id": "BvVerPG2pVCB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnsDntPDo2CG",
        "outputId": "5558af56-2e86-4d59-d06b-33ca69c0ddf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
            "       'Species'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "WUCGd_W6p1cu",
        "outputId": "90b85a8a-0d82-4a90-86ab-fec37bc4d396"
      },
      "execution_count": null,
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            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa\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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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1b7dd5dc-34c3-41c6-b249-6fc64a30b60c');\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",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\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\": \"SepalWidthCm\",\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\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\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\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\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": 15
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "display(df.describe())"
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        "colab": {
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          "height": 300
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        "id": "7zl_BXjjrUzM",
        "outputId": "c090775f-451e-4631-b2f4-443bcb70c819"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ],
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              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
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              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.445368</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.756293020494844,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          150.0,\n          75.5,\n          112.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08647211421483,\n        \"min\": 0.4335943113621737,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0540000000000003,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.835227940958106,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.758666666666666,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.63663424340991,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1986666666666668,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "cell_type": "code",
      "source": [
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        "id": "XMzn_g5eriRj",
        "outputId": "935b2838-883e-4584-f320-6539a89c8de2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df['Species'].value_counts()"
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      "metadata": {
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          "height": 209
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        "id": "HIXTonTBsPPw",
        "outputId": "f42f14aa-7b23-4b4c-d099-251626f47d09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "Name: count, dtype: int64"
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 18
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    {
      "cell_type": "code",
      "source": [
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
              "0    False          False         False          False         False    False\n",
              "1    False          False         False          False         False    False\n",
              "2    False          False         False          False         False    False\n",
              "3    False          False         False          False         False    False\n",
              "4    False          False         False          False         False    False\n",
              "..     ...            ...           ...            ...           ...      ...\n",
              "145  False          False         False          False         False    False\n",
              "146  False          False         False          False         False    False\n",
              "147  False          False         False          False         False    False\n",
              "148  False          False         False          False         False    False\n",
              "149  False          False         False          False         False    False\n",
              "\n",
              "[150 rows x 6 columns]"
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5dabf64f-4360-4e04-aad1-4c2388b704f1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-5dabf64f-4360-4e04-aad1-4c2388b704f1 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-5dabf64f-4360-4e04-aad1-4c2388b704f1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "2HR7dObwsrXq",
        "outputId": "0a529af5-2773-4ce7-de88-d003b773fc12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Id'}>,\n",
              "        <Axes: title={'center': 'SepalLengthCm'}>],\n",
              "       [<Axes: title={'center': 'SepalWidthCm'}>,\n",
              "        <Axes: title={'center': 'PetalLengthCm'}>],\n",
              "       [<Axes: title={'center': 'PetalWidthCm'}>, <Axes: >]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KDKdCHApvtDt",
        "outputId": "2f20735f-f926-4a2e-e41f-8e24ada42c1c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "colors = {'Iris-virginica': 'red', 'Iris-versicolor': 'orange', 'Iris-setosa': 'blue'}\n",
        "\n",
        "feature_pairs = [\n",
        "    ('SepalLengthCm', 'SepalWidthCm'),\n",
        "    ('PetalLengthCm', 'PetalWidthCm'),\n",
        "    ('SepalLengthCm', 'PetalLengthCm'),\n",
        "    ('SepalWidthCm', 'PetalWidthCm')\n",
        "]\n",
        "\n",
        "\n",
        "fig, axes = plt.subplots(2, 2, figsize=(10, 10))\n",
        "axes = axes.flatten()\n",
        "\n",
        "species_names = df['Species'].unique()\n",
        "\n",
        "for i, (x_col, y_col) in enumerate(feature_pairs):\n",
        "    ax = axes[i]\n",
        "    for species in species_names:\n",
        "        species_data = df[df['Species'] == species]\n",
        "        ax.scatter(\n",
        "            species_data[x_col],\n",
        "            species_data[y_col],\n",
        "            color=colors[species],\n",
        "            label=species\n",
        "        )\n",
        "    ax.set_xlabel(x_col)\n",
        "    ax.set_ylabel(y_col)\n",
        "    ax.set_title(f'{x_col} vs {y_col}')\n",
        "    ax.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Iris-sesota is seprated from the other 2 species with smaller petal lenth compared to the rest\n",
        "Iris-Virginca & Iris-Versicolor are better measured whith petal leanth & width and provide better sepration"
      ],
      "metadata": {
        "id": "iIyGDZ-Fydww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "numeric_df = df.select_dtypes(include=['number'])\n",
        "\n",
        "corr_matrix = numeric_df.corr()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Matrix of Iris Attributes')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "qlSRZe8Wz2tL",
        "outputId": "67f6c450-18da-4beb-ec47-6411d2e7463c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Petal  (length and width) are highly correlated with each other and also strongly correlated with sepal length. This indicates that petal measurements are very interconnected and also have a significant relationship with how long the sepal is.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "tnS2mj2t6cvJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3KBHdNFcqQKa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4974c14"
      },
      "source": [
        "Here's how the species have been encoded:\n",
        "*   Iris-setosa: 0\n",
        "*   Iris-versicolor: 1\n",
        "*   Iris-virginica: 2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[50:55]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9RRBZ5KS6ZPt",
        "outputId": "ddc60ce9-acc2-4b45-cc72-1157eb5ba70b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "50  51            7.0           3.2            4.7           1.4   \n",
              "51  52            6.4           3.2            4.5           1.5   \n",
              "52  53            6.9           3.1            4.9           1.5   \n",
              "53  54            5.5           2.3            4.0           1.3   \n",
              "54  55            6.5           2.8            4.6           1.5   \n",
              "\n",
              "            Species  \n",
              "50  Iris-versicolor  \n",
              "51  Iris-versicolor  \n",
              "52  Iris-versicolor  \n",
              "53  Iris-versicolor  \n",
              "54  Iris-versicolor  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
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              "      <th>SepalWidthCm</th>\n",
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              "      <td>7.0</td>\n",
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              "      <th>51</th>\n",
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              "      <td>6.4</td>\n",
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              "      <td>54</td>\n",
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              "      <th>54</th>\n",
              "      <td>55</td>\n",
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              "      <td>2.8</td>\n",
              "      <td>4.6</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Iris-versicolor</td>\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",
              "      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-49d1fd30-0401-45bb-a9b5-28c95d562748 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-49d1fd30-0401-45bb-a9b5-28c95d562748');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[50:55]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 51,\n        \"max\": 55,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          52,\n          55,\n          53\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.594138031100518,\n        \"min\": 5.5,\n        \"max\": 7.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.4,\n          6.5,\n          6.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38340579025361643,\n        \"min\": 2.3,\n        \"max\": 3.2,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.1,\n          2.8,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3361547262794323,\n        \"min\": 4.0,\n        \"max\": 4.9,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.5,\n          4.6,\n          4.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08944271909999157,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.5,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Iris-versicolor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The code for the 3rd class is (1) its not showing because its not in the first 5 (head) or last 5 (tail)"
      ],
      "metadata": {
        "id": "AvkSS2E-864h"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7e0c7dc8",
        "outputId": "ca580c73-cc23-4a7e-e8f5-f5d61775938b"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "df['Species_encoded'] = le.fit_transform(df['Species'])\n",
        "X = df[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']]\n",
        "y = df['Species_encoded']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (105, 4)\n",
            "X_test shape: (45, 4)\n",
            "y_train shape: (105,)\n",
            "y_test shape: (45,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "df['Species_encoded'] = le.fit_transform(df['Species'])\n",
        "\n",
        "\n",
        "print(\"  الكلاس:\")\n",
        "for index, class_name in enumerate(le.classes_):\n",
        "    print(f\"الرقم {index}  الفئة: {class_name}\")\n",
        "\n"
      ],
      "metadata": {
        "id": "i4S6iUu_8jTX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a7a5fd1-bb07-44a1-fddc-e776601e85f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  الكلاس:\n",
            "الرقم 0  الفئة: Iris-setosa\n",
            "الرقم 1  الفئة: Iris-versicolor\n",
            "الرقم 2  الفئة: Iris-virginica\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "code of third class is 2"
      ],
      "metadata": {
        "id": "569SiEvfOvK4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3CRXTxus66So"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f27258ef-c18d-470f-d12e-f76c47baeab8",
        "id": "SB2PBbAN662C"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "df['Species_Encoded'] = df['Species'].map({'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2})\n",
        "le = LabelEncoder()\n",
        "\n",
        "for column in df.columns:\n",
        "  if df[column].dtype == 'object':\n",
        "      df[column] = le.fit_transform(df[column])\n",
        "\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species  \\\n",
              "0   1            5.1           3.5            1.4           0.2        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   \n",
              "\n",
              "   Species_encoded  Species_Encoded  \n",
              "0                0                0  \n",
              "1                0                0  \n",
              "2                0                0  \n",
              "3                0                0  \n",
              "4                0                0  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
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              "      <th>Species_Encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\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",
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              "      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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              "      margin-bottom: 4px;\n",
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              "      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-50e28779-2b25-4ed8-ad32-05a2486d4228 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-50e28779-2b25-4ed8-ad32-05a2486d4228');\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",
              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8280661279778629,\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\": \"SepalWidthCm\",\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\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522617,\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\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008414,\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      \"column\": \"Species_encoded\",\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      \"column\": \"Species_Encoded\",\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": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Species'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "3FUgJLXGuAkM",
        "outputId": "cc64582a-01e4-4def-b1da-a31709ff502e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species\n",
              "0    50\n",
              "1    50\n",
              "2    50\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>Species</th>\n",
              "      <th></th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "\n",
        "if 'Species_encoded' in df.columns:\n",
        "    y = df['Species_encoded']\n",
        "else:\n",
        "    y = df['Species']\n",
        "\n",
        "\n",
        "X = df.drop(columns=['Species', 'Species_encoded', 'Id'], errors='ignore')\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jrFfo-grQBQB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6eb065fc-7f36-497b-974c-a72e6b3b3d14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "y_pred = knn.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwPKVRWbroAw",
        "outputId": "34a7f818-fd32-4741-d51f-27c194621575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      1.00      1.00        10\n",
            "           2       1.00      1.00      1.00        10\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}