1.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP+1fj4qtYe0JpKq8AhFe/G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "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/prrai1712/Gramener/blob/main/1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-yz04-klgCg",
        "outputId": "6782a988-b489-4b5a-c3af-be156a199ef8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.8.30)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.6)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.2.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.2.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.10)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "HEM30HnWnG2p",
        "outputId": "de791454-b735-406e-b910-8e088f173db8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5acc543a-490e-4966-bff7-ae5f25794386\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5acc543a-490e-4966-bff7-ae5f25794386\">\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",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    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 kaggle.json to kaggle (1).json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle (1).json': b'{\"username\":\"kittu17\",\"key\":\"1f38305a6144c87307f4937087ef8885\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content\"\n",
        "\n"
      ],
      "metadata": {
        "id": "4vTDpcGznZ_e"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d mustafaali96/weight-height\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrlugnhPnnNw",
        "outputId": "ad0b9864-1d59-4270-9eb3-38c1ba1cfbce"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /content/kaggle.json'\n",
            "Dataset URL: https://www.kaggle.com/datasets/mustafaali96/weight-height\n",
            "License(s): unknown\n",
            "Downloading weight-height.zip to /content\n",
            "  0% 0.00/167k [00:00<?, ?B/s]\n",
            "100% 167k/167k [00:00<00:00, 69.4MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip weight-height.zip\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y-_pLCConsbu",
        "outputId": "91095f6e-ffea-48b3-ee8c-7052748179be"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  weight-height.zip\n",
            "  inflating: weight-height.csv       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('weight-height.csv')\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "c4Sph3jvn67q",
        "outputId": "178d6fa3-d701-4b42-b887-f64e3199d914"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gender     Height      Weight\n",
              "0   Male  73.847017  241.893563\n",
              "1   Male  68.781904  162.310473\n",
              "2   Male  74.110105  212.740856\n",
              "3   Male  71.730978  220.042470\n",
              "4   Male  69.881796  206.349801"
            ],
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              "        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",
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              "    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-d21712be-1aa1-48d2-805a-ab4fa7a0f2a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8475281207732324,\n        \"min\": 54.2631333250971,\n        \"max\": 78.9987423463896,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          68.6872321923365,\n          68.8293337723017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.108439006519546,\n        \"min\": 64.700126712753,\n        \"max\": 269.989698505106,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          173.115812588004,\n          195.162042097346\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def handle_missing_values(df):\n",
        "    num_cols = df.select_dtypes(include=['number']).columns\n",
        "\n",
        "    df_mean = df.copy()\n",
        "    df_median = df.copy()\n",
        "    df_mode = df.copy()\n",
        "\n",
        "    df_mean[num_cols] = df_mean[num_cols].fillna(df_mean[num_cols].mean())\n",
        "    df_median[num_cols] = df_median[num_cols].fillna(df_median[num_cols].median())\n",
        "    df_mode[num_cols] = df_mode[num_cols].fillna(df_mode[num_cols].mode().iloc[0])\n",
        "\n",
        "    return df_mean, df_median, df_mode\n",
        "\n",
        "df_mean, df_median, df_mode = handle_missing_values(df)\n",
        "\n",
        "df_mean.to_csv('weight-height_with_mean.csv', index=False)\n",
        "df_median.to_csv('weight-height_with_median.csv', index=False)\n",
        "df_mode.to_csv('weight-height_with_mode.csv', index=False)\n",
        "\n",
        "from google.colab import files\n",
        "files.download('weight-height_with_mean.csv')\n",
        "files.download('weight-height_with_median.csv')\n",
        "files.download('weight-height_with_mode.csv')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "JrufNVfIoG4C",
        "outputId": "060a94c0-658b-4240-a968-05d2e46da787"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_30e79029-d794-4508-ac09-4f67922f5f32\", \"weight-height_with_mean.csv\", 397384)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_b9068668-c722-496d-9146-3623d6e77ec1\", \"weight-height_with_median.csv\", 397384)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_7d4294f2-bbde-49d3-85db-028aa44d56a2\", \"weight-height_with_mode.csv\", 397384)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LAB 2**"
      ],
      "metadata": {
        "id": "I9ghIrH5pgvj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "\n",
        "df = pd.read_csv('weight-height.csv')\n",
        "num_cols = df.select_dtypes(include=['number']).columns\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df_min_max = df.copy()\n",
        "df_min_max[num_cols] = scaler.fit_transform(df_min_max[num_cols])\n",
        "\n",
        "scaler = StandardScaler()\n",
        "df_o1 = df.copy()\n",
        "df_o1[num_cols] = scaler.fit_transform(df_o1[num_cols])\n",
        "\n",
        "df_min_max.to_csv('weight-height_min_max.csv', index=False)\n",
        "df_o1.to_csv('weight-height_o1.csv', index=False)\n",
        "\n",
        "from google.colab import files\n",
        "files.download('weight-height_min_max.csv')\n",
        "files.download('weight-height_o1.csv')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "Y9tYnt6VoYtj",
        "outputId": "37113330-5583-452f-e045-247cbcd2e0e7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_95e2282c-8f8f-4f41-91d1-bb2ede6d05fc\", \"weight-height_min_max.csv\", 443427)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_f8ef4c37-ebc7-4317-8e20-c3544892bf3e\", \"weight-height_o1.csv\", 451647)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LAB 3**"
      ],
      "metadata": {
        "id": "qHcZs9tGqwcy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.stats import entropy\n",
        "\n",
        "def ent(p):\n",
        "    return entropy(p, base=2)\n",
        "\n",
        "def joint_ent(p_x, p_y):\n",
        "    p_xy = np.outer(p_x, p_y).flatten()\n",
        "    return entropy(p_xy, base=2)\n",
        "\n",
        "def cond_ent(p_x, p_y):\n",
        "    return joint_ent(p_x, p_y) - ent(p_y)\n",
        "\n",
        "def info_gain(p_x, p_y):\n",
        "    return ent(p_x) - cond_ent(p_x, p_y)\n",
        "\n",
        "def mutual_info(p_x, p_y):\n",
        "    return ent(p_x) + ent(p_y) - joint_ent(p_x, p_y)\n",
        "\n",
        "def gini(p):\n",
        "    return 1 - np.sum(p ** 2)\n",
        "\n",
        "p_x = np.array([0.6, 0.4])\n",
        "p_y = np.array([0.7, 0.3])\n",
        "\n",
        "h_x = ent(p_x)\n",
        "h_y = ent(p_y)\n",
        "h_xy = joint_ent(p_x, p_y)\n",
        "h_x_given_y = cond_ent(p_x, p_y)\n",
        "ig = info_gain(p_x, p_y)\n",
        "mi = mutual_info(p_x, p_y)\n",
        "g_x = gini(p_x)\n",
        "g_y = gini(p_y)\n",
        "\n",
        "print(\"H(X):\", h_x)\n",
        "print(\"H(Y):\", h_y)\n",
        "print(\"H(X,Y):\", h_xy)\n",
        "print(\"H(X|Y):\", h_x_given_y)\n",
        "print(\"IG(X|Y):\", ig)\n",
        "print(\"I(X;Y):\", mi)\n",
        "print(\"Gini X:\", g_x)\n",
        "print(\"Gini Y:\", g_y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBMCKBrNqzlB",
        "outputId": "762e8942-63a4-4e20-c26f-0b853e0f7a71"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "H(X): 0.9709505944546688\n",
            "H(Y): 0.8812908992306927\n",
            "H(X,Y): 1.8522414936853613\n",
            "H(X|Y): 0.9709505944546686\n",
            "IG(X|Y): 2.220446049250313e-16\n",
            "I(X;Y): 2.220446049250313e-16\n",
            "Gini X: 0.48\n",
            "Gini Y: 0.42000000000000004\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LAB 4**"
      ],
      "metadata": {
        "id": "LdJba2bH4PhM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "d = {'M': [1, 1, 0, 1, 0],\n",
        "     'B': [1, 0, 1, 1, 1],\n",
        "     'Bt': [0, 1, 1, 0, 1],\n",
        "     'Br': [0, 0, 1, 1, 0]}\n",
        "df = pd.DataFrame(d)\n",
        "\n",
        "df = df.astype(bool)\n",
        "\n",
        "f = apriori(df, min_support=0.6, use_colnames=True)\n",
        "r = association_rules(f, metric=\"confidence\", min_threshold=0.7)\n",
        "\n",
        "print(\"Freq Itemsets:\\n\", f)\n",
        "print(\"\\nRules:\\n\", r)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyMHVTsw4SNC",
        "outputId": "06f848d6-dbf4-4675-a5c3-c119490fe83b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Freq Itemsets:\n",
            "    support itemsets\n",
            "0      0.6      (M)\n",
            "1      0.8      (B)\n",
            "2      0.6     (Bt)\n",
            "\n",
            "Rules:\n",
            " Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LAB 5**"
      ],
      "metadata": {
        "id": "-Jikuo9V5pos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"weight-height.csv\")\n",
        "X = df[['Height']]\n",
        "y = df['Weight']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "mdl = LinearRegression()\n",
        "mdl.fit(X_train, y_train)\n",
        "\n",
        "y_pred = mdl.predict(X_test)\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(\"Coefficients:\", mdl.coef_)\n",
        "print(\"Intercept:\", mdl.intercept_)\n",
        "print(\"MSE:\", mse)\n",
        "print(\"R^2 Score:\", r2)\n",
        "\n",
        "plt.scatter(X_test, y_test, color='blue', label=\"Actual Data\")\n",
        "plt.plot(X_test, y_pred, color='red', linewidth=2, label=\"Regression Line\")\n",
        "plt.xlabel(\"Height\")\n",
        "plt.ylabel(\"Weight\")\n",
        "plt.title(\"Height vs. Weight - Linear Regression\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "2HA6k2oT5rRm",
        "outputId": "94ad2668-beb7-463a-955d-2d2e0d7cc274"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficients: [7.70936331]\n",
            "Intercept: -350.1904028560757\n",
            "MSE: 146.5367721395743\n",
            "R^2 Score: 0.8581952493935256\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LAB 6**"
      ],
      "metadata": {
        "id": "BcnTq1yb66VH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"weight-height.csv\")\n",
        "df['Gender'] = df['Gender'].map({'Male': 1, 'Female': 0})\n",
        "\n",
        "X = df[['Height', 'Weight']]\n",
        "y = df['Gender']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
        "\n",
        "mdl = LogisticRegression()\n",
        "mdl.fit(X_train, y_train)\n",
        "\n",
        "y_pred = mdl.predict(X_test)\n",
        "\n",
        "acc = accuracy_score(y_test, y_pred)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(\"Accuracy:\", acc)\n",
        "print(\"Confusion Matrix:\\n\", cm)\n",
        "\n",
        "plt.scatter(X_test['Height'], X_test['Weight'], c=y_pred, cmap='coolwarm', label='Predictions')\n",
        "plt.xlabel(\"Height\")\n",
        "plt.ylabel(\"Weight\")\n",
        "plt.title(\"Logistic Regression - Gender Classification\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "asBs72nq7Alk",
        "outputId": "060fd9d9-1c53-47ea-c861-b6ec79340165"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9225\n",
            "Confusion Matrix:\n",
            " [[937  75]\n",
            " [ 80 908]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}