CodsoftTask2a.ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPRJDLw7MkGSmjcBPiP3+10",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ramshamashood/CODSOFT/blob/main/CodsoftTask2a.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "BCS2BK7944wx",
        "outputId": "2b607b8a-d8f0-42a1-e42d-3d16be0ec90e"
      },
      "outputs": [
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              "<IPython.core.display.HTML object>"
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              "\n",
              "     <input type=\"file\" id=\"files-48fea046-66b5-4054-b2c3-c8c97ba336f4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-48fea046-66b5-4054-b2c3-c8c97ba336f4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
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              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Iris.csv to Iris (1).csv\n",
            "User uploaded file \"Iris (1).csv\" with length 4617 bytes\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "df = pd.read_csv('/content/Iris.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EGDPXz-56g4M",
        "outputId": "382c4f6b-fc25-4f59-a714-f8dcd28ce4b5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
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              "      <td>5.1</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",
              "          '<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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              "      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",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()\n",
        "print(missing_values)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MAH5Pjk76mHV",
        "outputId": "35aab104-23d4-4ff6-9bd3-a0f14603bc3d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sepal_length    0\n",
            "sepal_width     0\n",
            "petal_length    0\n",
            "petal_width     0\n",
            "species         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTCG5u498N6D",
        "outputId": "5272cbdc-dbed-4ab9-deab-b95952031598"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_length    35\n",
              "sepal_width     23\n",
              "petal_length    43\n",
              "petal_width     22\n",
              "species          3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AMhDTv1r8UFg",
        "outputId": "ec3c92d9-2555-465e-d9f1-892972a20f9c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.054000      3.758667     1.198667\n",
              "std        0.828066     0.433594      1.764420     0.763161\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b9fddb7f-5191-485a-bf1d-22e49849d294\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\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>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\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>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>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9fddb7f-5191-485a-bf1d-22e49849d294')\"\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-b9fddb7f-5191-485a-bf1d-22e49849d294 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-b9fddb7f-5191-485a-bf1d-22e49849d294');\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 id=\"df-3195339d-3e5c-4eff-894e-598e73160e75\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3195339d-3e5c-4eff-894e-598e73160e75')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3195339d-3e5c-4eff-894e-598e73160e75 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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "df[\"species\"] = le.fit_transform(df[\"species\"])\n",
        "df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yEwqm2E19dqP",
        "outputId": "055ae238-20d5-4f1a-cf50-97664a57c184"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width  species\n",
              "0             5.1          3.5           1.4          0.2        0\n",
              "1             4.9          3.0           1.4          0.2        0\n",
              "2             4.7          3.2           1.3          0.2        0\n",
              "3             4.6          3.1           1.5          0.2        0\n",
              "4             5.0          3.6           1.4          0.2        0\n",
              "..            ...          ...           ...          ...      ...\n",
              "145           6.7          3.0           5.2          2.3        2\n",
              "146           6.3          2.5           5.0          1.9        2\n",
              "147           6.5          3.0           5.2          2.0        2\n",
              "148           6.2          3.4           5.4          2.3        2\n",
              "149           5.9          3.0           5.1          1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9db6cb4-31ca-4481-b206-6f593e62cc01\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 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-f9db6cb4-31ca-4481-b206-6f593e62cc01')\"\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-f9db6cb4-31ca-4481-b206-6f593e62cc01 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-f9db6cb4-31ca-4481-b206-6f593e62cc01');\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 id=\"df-c2e6142f-d877-4a95-88ba-8dde12379875\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2e6142f-d877-4a95-88ba-8dde12379875')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <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",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2e6142f-d877-4a95-88ba-8dde12379875 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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask = np.zeros_like(df.corr())\n",
        "triangle_indices = np.triu_indices_from(mask)\n",
        "mask[triangle_indices] = True\n",
        "mask\n",
        "plt.figure(figsize=(16,10))\n",
        "sns.heatmap(df.corr(), mask=mask, annot=True, annot_kws={\"size\": 10})\n",
        "sns.set_style('white')\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "-nBGhKBT8d26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "outputId": "de391bb7-6771-4f0b-ac0e-a7695a6a33e0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x='sepal_length', y='sepal_width', data=df, hue=\"species\", palette=\"Set2\", s=80, alpha=0.8)\n",
        "plt.title(\"Scatter Plot of Sepal Length vs Sepal Width\")\n",
        "plt.xlabel(\"Sepal Length (cm)\")\n",
        "plt.ylabel(\"Sepal Width (cm)\")\n",
        "plt.legend(title='species')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qsbejXta9qzH",
        "outputId": "b285d9a7-3a58-47db-ddd3-b4ee4430c19b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
        "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
        "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn.tree import export_graphviz\n",
        "from six import StringIO\n",
        "from IPython.display import Image\n",
        "import pydotplus\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix, ConfusionMatrixDisplay, RocCurveDisplay\n",
        "\n",
        "feature_cols=[\"sepal_length\t\",\"sepal_width\t\",\"petal_length\t\",\"petal_width\"]\n",
        "X=df.drop([\"species\"],axis=1)\n",
        "y=df[\"species\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=25) # 70% training and 30% test\n",
        "\n",
        "clf = DecisionTreeClassifier()\n",
        "\n",
        "clf = clf.fit(X_train,y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "print('Accuracy: ', accuracy_score(y_test, y_pred))\n",
        "print('Precision: ', precision_score(y_test, y_pred,average='micro'))\n",
        "print('Recall: ', recall_score(y_test, y_pred, average='micro'))\n",
        "print('F1 Score: ', f1_score(y_test, y_pred,average='micro'))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PTVnkKM0-vr6",
        "outputId": "7f56e6f5-44d4-472e-e283-d752541fb846"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9555555555555556\n",
            "Precision:  0.9555555555555556\n",
            "Recall:  0.9555555555555556\n",
            "F1 Score:  0.9555555555555556\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import graphviz\n",
        "from sklearn import tree\n",
        "\n",
        "# DOT data\n",
        "dot_data = tree.export_graphviz(clf, out_file=None,\n",
        "                                feature_names=feature_cols,\n",
        "                                class_names=['0','1','2'],\n",
        "                                filled=True)\n",
        "\n",
        "# Draw graph\n",
        "graph = graphviz.Source(dot_data, format=\"png\")\n",
        "graph"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3TZPCVa0GYMh",
        "outputId": "337d654e-cc6e-47e2-c9c3-a0bc75887ac4"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<graphviz.sources.Source at 0x7ce584bbe3b0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "T86JtHT4F_y6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
        "disp.plot();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "1Nmjx_ePFWZb",
        "outputId": "51273f65-6c4b-4955-d7d2-2f6b1f419ea9"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}