23_01_2024.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPzjNmkZlYwxUWuEjJoVpTM",
      "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/Darshan0902/EDUMITRA-./blob/main/23_01_2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "3i0-VqPLLZ7H"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =pd.read_csv(\"/content/MOCK_DATA.csv\")"
      ],
      "metadata": {
        "id": "vcDBloQ-L0Ee"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "sXbQ3GyYL3pb",
        "outputId": "3a59714f-351d-4253-ef4c-8cf67e8cde9c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id    first_name                            email       gender  \\\n",
              "0       1         Daune            dhellwich0@devhub.com       Female   \n",
              "1       2       Maridel           mhendrick1@oaic.gov.au       Female   \n",
              "2       3     Mehetabel  mhandscomb2@creativecommons.org       Female   \n",
              "3       4         Nancy        ntrenholme3@homestead.com       Female   \n",
              "4       5           Pat           pjanca4@deviantart.com         Male   \n",
              "..    ...           ...                              ...          ...   \n",
              "995   996      Papageno            pthemannrn@devhub.com         Male   \n",
              "996   997        Hebert             hknobellro@google.nl         Male   \n",
              "997   998         Kiley      knovotnarp@businessweek.com         Male   \n",
              "998   999  Maximilianus        mvasilmanovrq@sina.com.cn  Genderqueer   \n",
              "999  1000        Rozele                 rbirkinrr@goo.gl       Female   \n",
              "\n",
              "                          Skills  \n",
              "0                       Robotics  \n",
              "1         Mobile App Development  \n",
              "2                Cloud Computing  \n",
              "3               Game Development  \n",
              "4                Web Development  \n",
              "..                           ...  \n",
              "995                       Python  \n",
              "996              Computer Vision  \n",
              "997  Natural Language Processing  \n",
              "998         Full Stack Developer  \n",
              "999                     Robotics  \n",
              "\n",
              "[1000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c02125ea-8c79-4f15-a530-b238ba493c31\" 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>id</th>\n",
              "      <th>first_name</th>\n",
              "      <th>email</th>\n",
              "      <th>gender</th>\n",
              "      <th>Skills</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Daune</td>\n",
              "      <td>dhellwich0@devhub.com</td>\n",
              "      <td>Female</td>\n",
              "      <td>Robotics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Maridel</td>\n",
              "      <td>mhendrick1@oaic.gov.au</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mobile App Development</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Mehetabel</td>\n",
              "      <td>mhandscomb2@creativecommons.org</td>\n",
              "      <td>Female</td>\n",
              "      <td>Cloud Computing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Nancy</td>\n",
              "      <td>ntrenholme3@homestead.com</td>\n",
              "      <td>Female</td>\n",
              "      <td>Game Development</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Pat</td>\n",
              "      <td>pjanca4@deviantart.com</td>\n",
              "      <td>Male</td>\n",
              "      <td>Web Development</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>995</th>\n",
              "      <td>996</td>\n",
              "      <td>Papageno</td>\n",
              "      <td>pthemannrn@devhub.com</td>\n",
              "      <td>Male</td>\n",
              "      <td>Python</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>997</td>\n",
              "      <td>Hebert</td>\n",
              "      <td>hknobellro@google.nl</td>\n",
              "      <td>Male</td>\n",
              "      <td>Computer Vision</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>998</td>\n",
              "      <td>Kiley</td>\n",
              "      <td>knovotnarp@businessweek.com</td>\n",
              "      <td>Male</td>\n",
              "      <td>Natural Language Processing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>999</td>\n",
              "      <td>Maximilianus</td>\n",
              "      <td>mvasilmanovrq@sina.com.cn</td>\n",
              "      <td>Genderqueer</td>\n",
              "      <td>Full Stack Developer</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>1000</td>\n",
              "      <td>Rozele</td>\n",
              "      <td>rbirkinrr@goo.gl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Robotics</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 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-c02125ea-8c79-4f15-a530-b238ba493c31')\"\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-c02125ea-8c79-4f15-a530-b238ba493c31 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-c02125ea-8c79-4f15-a530-b238ba493c31');\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-0cb8d77c-109a-4713-ad12-641259c96e7a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0cb8d77c-109a-4713-ad12-641259c96e7a')\"\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-0cb8d77c-109a-4713-ad12-641259c96e7a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_fc2753ed-8111-4c6e-9a0d-7576110c97e1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_fc2753ed-8111-4c6e-9a0d-7576110c97e1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\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\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 288,\n        \"min\": 1,\n        \"max\": 1000,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          522,\n          738,\n          741\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"first_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 938,\n        \"samples\": [\n          \"Thayne\",\n          \"Hinze\",\n          \"Burke\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"lhoyseh@hp.com\",\n          \"bstovekh@eepurl.com\",\n          \"ngronallerkk@technorati.com\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Male\",\n          \"Polygender\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Skills\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"C++\",\n          \"Artificial Intelligence\",\n          \"Robotics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# FIXED.\n"
      ],
      "metadata": {
        "id": "6ZOKnOWlsvQ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "\n",
        "# Create a dictionary to store users indexed by their skills\n",
        "users_by_skill = defaultdict(list)\n",
        "for _, row in df.iterrows():\n",
        "    users_by_skill[row['Skills']].append(row['id'])\n",
        "\n",
        "# Prepare the data for the decision tree classifier\n",
        "X = df['Skills'].values.reshape(-1, 1)  # Feature: Skills\n",
        "y = df['id']  # Target: User ID\n",
        "\n",
        "# One-hot encode the skills\n",
        "encoder = OneHotEncoder(handle_unknown='ignore')  # Handle unknown categories gracefully\n",
        "X_encoded = encoder.fit_transform(X)\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_encoded, y)\n",
        "\n",
        "# Function to recommend users based on the input skills\n",
        "def recommend_users_for_skills(skills):\n",
        "    # Encode the input skills\n",
        "    skills_encoded = encoder.transform([[skill] for skill in skills])\n",
        "\n",
        "    # Predict users with similar skills using the trained classifier\n",
        "    predicted_users = clf.predict(skills_encoded)\n",
        "\n",
        "    # Get all users with common skills\n",
        "    recommended_user_ids = set()\n",
        "    for user_id in predicted_users:\n",
        "        recommended_user_ids.update(users_by_skill[df.loc[df['id'] == user_id, 'Skills'].values[0]])\n",
        "    return recommended_user_ids\n",
        "\n",
        "# Get user input for skills\n",
        "selected_skills = input(\"Select your skills (comma-separated): \").split(',')\n",
        "\n",
        "# Get recommended users for the selected skills\n",
        "recommended_user_ids = recommend_users_for_skills(selected_skills)\n",
        "recommended_users_info = df[df['id'].isin(recommended_user_ids)][['id', 'first_name', 'email', 'gender']]\n",
        "\n",
        "# Print recommended users\n",
        "if not recommended_users_info.empty:\n",
        "    print(\"Recommended users:\")\n",
        "    print(recommended_users_info)\n",
        "else:\n",
        "    print(\"No users found with the selected skills.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yy11u6svL73r",
        "outputId": "69ab67cd-4ee0-4721-e10c-3b626e0fe6a2"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select your skills (comma-separated): Robotics\n",
            "Recommended users:\n",
            "       id   first_name                           email       gender\n",
            "0       1        Daune           dhellwich0@devhub.com       Female\n",
            "11     12         Dyan          dbaldackb@netscape.com       Female\n",
            "22     23      Breanne          ballbrookm@tinypic.com       Female\n",
            "79     80       Regina               rgreally27@360.cn       Female\n",
            "197   198      Matelda         mgjerde5h@discovery.com       Female\n",
            "213   214         Fred        fboatwright5x@toplist.cz         Male\n",
            "243   244     Beverley           bkeal6r@parallels.com       Female\n",
            "250   251         Codi          cbette6y@reference.com       Female\n",
            "255   256     Aloysius           asaunt73@google.co.uk         Male\n",
            "284   285         Chev        ccappineer7w@reuters.com         Male\n",
            "296   297        Berky              btolley88@cnet.com         Male\n",
            "331   332    Robinetta         radds97@accuweather.com      Agender\n",
            "344   345       Philis  pmckerley9k@chicagotribune.com   Polygender\n",
            "348   349      Sergent  schippin9o@merriam-webster.com         Male\n",
            "368   369         Debi           dmingamea8@ustream.tv   Polygender\n",
            "377   378     Brigitte               bbikkerah@ucoz.ru       Female\n",
            "414   415        Angel              aschoffelbi@hp.com         Male\n",
            "415   416          Wes       wmcgerraghtybj@eepurl.com         Male\n",
            "435   436     Jerrilee                  jselfc3@qq.com       Female\n",
            "460   461      Darelle                 dstihlcs@qq.com       Female\n",
            "494   495        Hildy   hneillansdq@blogtalkradio.com       Female\n",
            "500   501     Fernanda            fkubczakdw@blogs.com       Female\n",
            "503   504      Woodman                 woretdz@mapy.cz         Male\n",
            "519   520      Rebecka         rferrieresef@abc.net.au       Female\n",
            "522   523        Julio        janthoniesei@dropbox.com         Male\n",
            "535   536      Enrique              evidaev@senate.gov         Male\n",
            "538   539      Cherida          crubinowitschey@gov.uk  Genderqueer\n",
            "576   577       Hamnet           hkornasg0@storify.com      Agender\n",
            "600   601     Crawford            cmichelgo@ustream.tv         Male\n",
            "629   630    Remington        rdangelihh@instagram.com         Male\n",
            "642   643        Salim          sstowhu@vistaprint.com      Agender\n",
            "719   720         Pepe             pgouneljz@google.fr         Male\n",
            "720   721        Perry          phuttonk0@mapquest.com         Male\n",
            "737   738       Bridie             bstovekh@eepurl.com  Genderfluid\n",
            "766   767      Ronnica             rshoriela@youku.com       Female\n",
            "770   771        Court           cizakovle@mozilla.com         Male\n",
            "806   807      Fayette             flodevickme@umn.edu       Female\n",
            "828   829  Constantino        charbertn0@tuttocitta.it         Male\n",
            "911   912          Ben         bmoncarrpb@amazon.co.uk         Male\n",
            "929   930      Adeline              ajohnstonpt@si.edu       Female\n",
            "933   934     Lauretta           lpudneypx@auda.org.au       Female\n",
            "940   941        Garry            gwiltshearq4@mail.ru     Bigender\n",
            "948   949       Tybalt          tsatterfittqc@digg.com         Male\n",
            "950   951        Upton         ukeemarqe@sitemeter.com         Male\n",
            "971   972        Willi         whegertyqz@edublogs.org         Male\n",
            "989   990       Loella                  lmedlerrh@t.co       Female\n",
            "999  1000       Rozele                rbirkinrr@goo.gl       Female\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dropdown"
      ],
      "metadata": {
        "id": "tZMnIaOpR6v4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install ipywidgets\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQz6uP-eR9YF",
        "outputId": "7c78d7f4-43f9-4a33-9d0e-8af92d20314e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: ipywidgets in /usr/local/lib/python3.10/dist-packages (7.7.1)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (5.5.6)\n",
            "Requirement already satisfied: ipython-genutils~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (0.2.0)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (5.7.1)\n",
            "Requirement already satisfied: widgetsnbextension~=3.6.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (3.6.6)\n",
            "Requirement already satisfied: ipython>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (7.34.0)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from ipywidgets) (3.0.10)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets) (6.1.12)\n",
            "Requirement already satisfied: tornado>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipykernel>=4.5.1->ipywidgets) (6.3.2)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (67.7.2)\n",
            "Collecting jedi>=0.16 (from ipython>=4.0.0->ipywidgets)\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (3.0.43)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython>=4.0.0->ipywidgets) (4.9.0)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.10/dist-packages (from widgetsnbextension~=3.6.0->ipywidgets) (6.5.5)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython>=4.0.0->ipywidgets) (0.8.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.1.3)\n",
            "Requirement already satisfied: pyzmq<25,>=17 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (23.2.1)\n",
            "Requirement already satisfied: argon2-cffi in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (23.1.0)\n",
            "Requirement already satisfied: jupyter-core>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (5.7.1)\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (5.9.2)\n",
            "Requirement already satisfied: nbconvert>=5 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (6.5.4)\n",
            "Requirement already satisfied: nest-asyncio>=1.5 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.6.0)\n",
            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.8.2)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.18.0)\n",
            "Requirement already satisfied: prometheus-client in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.20.0)\n",
            "Requirement already satisfied: nbclassic>=0.4.7 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.0.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets) (2.8.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython>=4.0.0->ipywidgets) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=4.0.0->ipywidgets) (0.2.13)\n",
            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.6.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (4.2.0)\n",
            "Requirement already satisfied: jupyter-server>=1.8 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.24.0)\n",
            "Requirement already satisfied: notebook-shim>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.2.4)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (4.9.4)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (4.12.3)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (6.1.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.4)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.1.5)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.8.4)\n",
            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.9.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (23.2)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.5.1)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.2.1)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.19.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (4.19.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.1->jupyter-client->ipykernel>=4.5.1->ipywidgets) (1.16.0)\n",
            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (21.2.0)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.33.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.18.0)\n",
            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.7.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.7.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.16.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.5)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (0.5.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (3.6)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (1.2.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets) (2.21)\n",
            "Installing collected packages: jedi\n",
            "Successfully installed jedi-0.19.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "from ipywidgets import interact_manual"
      ],
      "metadata": {
        "id": "nOsdVdxHL_VT"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "\n",
        "# Create a dictionary to store users indexed by their skills\n",
        "users_by_skill = defaultdict(list)\n",
        "for _, row in df.iterrows():\n",
        "    users_by_skill[row['Skills']].append(row['id'])\n",
        "\n",
        "# Prepare the data for the decision tree classifier\n",
        "X = df['Skills'].values.reshape(-1, 1)  # Feature: Skills\n",
        "y = df['id']  # Target: User ID\n",
        "\n",
        "# One-hot encode the skills\n",
        "encoder = OneHotEncoder(handle_unknown='ignore')  # Handle unknown categories gracefully\n",
        "X_encoded = encoder.fit_transform(X)\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_encoded, y)\n",
        "\n",
        "# Function to recommend users based on the input skills\n",
        "def recommend_users_for_skills(skills):\n",
        "    recommended_users = []\n",
        "    for skill in skills:\n",
        "        # Encode the input skill\n",
        "        skill_encoded = encoder.transform([[skill]])\n",
        "\n",
        "        # Predict users with similar skills using the trained classifier\n",
        "        predicted_users = clf.predict(skill_encoded)\n",
        "        recommended_users.append(predicted_users)\n",
        "\n",
        "    return recommended_users\n",
        "\n",
        "# Get user input for skills\n",
        "selected_skills = input(\"Select your skills (comma-separated): \").split(',')\n",
        "\n",
        "# Get recommended users for the selected skills\n",
        "recommended_users_info = []\n",
        "for skill in selected_skills:\n",
        "    recommended_user_ids = recommend_users_for_skills(skill)\n",
        "    recommended_users_info.append(df[df['id'].isin(recommended_user_ids)][['id', 'first_name', 'email', 'gender']])\n",
        "\n",
        "# Print recommended users for each skill\n",
        "for i, skill in enumerate(selected_skills):\n",
        "    recommended_users = recommended_users_info[i]\n",
        "    if not recommended_users.empty:\n",
        "        print(f\"Recommended users for skill '{skill}':\")\n",
        "        print(recommended_users)\n",
        "    else:\n",
        "        print(f\"No users found with skill '{skill}'.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mRTss3fRult",
        "outputId": "3b887f8a-df8c-4066-c3ec-8e74f080f48c"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select your skills (comma-separated): Robotics\n",
            "No users found with skill 'Robotics'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multiple users  :"
      ],
      "metadata": {
        "id": "x7rNbW3MdWGK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary to store users indexed by their skills\n",
        "users_by_skill = defaultdict(list)\n",
        "for _, row in df.iterrows():\n",
        "    for skill in row['Skills']:\n",
        "        users_by_skill[skill].append(row['id'])\n",
        "\n",
        "# Prepare the data for the decision tree classifier\n",
        "X = df['Skills'].explode().values.reshape(-1, 1)  # Feature: Skills (exploded to handle multiple skills per user)\n",
        "y = df['id']  # Target: User ID\n",
        "\n",
        "# One-hot encode the skills\n",
        "encoder = OneHotEncoder(handle_unknown='ignore')  # Handle unknown categories gracefully\n",
        "X_encoded = encoder.fit_transform(X)\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_encoded, y)\n",
        "\n",
        "# Function to recommend users based on the input skills\n",
        "def recommend_users_for_skills(skills):\n",
        "    recommended_users = set()\n",
        "    for skill in skills:\n",
        "        # Encode the input skill\n",
        "        skill_encoded = encoder.transform([[skill]])\n",
        "\n",
        "        # Predict users with similar skills using the trained classifier\n",
        "        predicted_users = clf.predict(skill_encoded)\n",
        "        recommended_users.update(predicted_users)\n",
        "\n",
        "    return recommended_users\n",
        "\n",
        "# Get user input for skills\n",
        "selected_skills = input(\"Select your skills (comma-separated): \").split(',')\n",
        "\n",
        "# Get recommended users for the selected skills\n",
        "recommended_user_ids = recommend_users_for_skills(selected_skills)\n",
        "\n",
        "# Display recommended users\n",
        "recommended_users_info = df[df['id'].isin(recommended_user_ids)][['id', 'first_name', 'email', 'gender']]\n",
        "if not recommended_users_info.empty:\n",
        "    print(\"Recommended users:\")\n",
        "    print(recommended_users_info)\n",
        "else:\n",
        "    print(\"No users found with the selected skills.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HrGqRDoYWsFP",
        "outputId": "f7e6d20d-4d55-4020-94c9-397a63af49f9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select your skills (comma-separated): Robotics\n",
            "Recommended users:\n",
            "   id first_name                  email  gender\n",
            "0   1      Daune  dhellwich0@devhub.com  Female\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ipywidgets import Button, Output, VBox\n"
      ],
      "metadata": {
        "id": "WfXBxeBTdYL9"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a dictionary to store users indexed by their skills\n",
        "users_by_skill = defaultdict(list)\n",
        "for _, row in df.iterrows():\n",
        "    for skill in row['Skills']:\n",
        "        users_by_skill[skill].append(row['id'])\n",
        "\n",
        "# Prepare the data for the decision tree classifier\n",
        "X = df['Skills'].values.reshape(-1, 1)  # Feature: Skills\n",
        "y = df['id']  # Target: User ID\n",
        "\n",
        "# One-hot encode the skills\n",
        "encoder = OneHotEncoder(handle_unknown='ignore')  # Handle unknown categories gracefully\n",
        "X_encoded = encoder.fit_transform(X)\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_encoded, y)\n",
        "\n",
        "# Function to recommend users based on the input skills\n",
        "def recommend_users_for_skills(skills):\n",
        "    recommended_users = []\n",
        "    for skill in skills:\n",
        "        # Encode the input skill\n",
        "        skill_encoded = encoder.transform([[skill]])\n",
        "\n",
        "        # Predict users with similar skills using the trained classifier\n",
        "        predicted_users = clf.predict(skill_encoded)\n",
        "        recommended_users.extend(predicted_users)  # Extend the list with predicted users\n",
        "\n",
        "    return recommended_users\n",
        "\n",
        "# Get user input for skills\n",
        "selected_skills = input(\"Select your skills (comma-separated): \").split(',')\n",
        "\n",
        "# Get recommended users for the selected skills\n",
        "recommended_users_info = []\n",
        "for skill in selected_skills:\n",
        "    recommended_user_ids = recommend_users_for_skills(skill)\n",
        "    recommended_users_info.append(df[df['id'].isin(recommended_user_ids)][['id', 'first_name', 'email', 'gender']])\n",
        "\n",
        "# Print recommended users for each skill\n",
        "for i, skill in enumerate(selected_skills):\n",
        "    recommended_users = recommended_users_info[i]\n",
        "    if not recommended_users.empty:\n",
        "        print(f\"Recommended users for skill '{skill}':\")\n",
        "        print(recommended_users)\n",
        "    else:\n",
        "        print(f\"No users found with skill '{skill}'.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yk8cY3Dd_FJ",
        "outputId": "2b417096-4798-42de-a708-2e1e1cb5f807"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select your skills (comma-separated): PHP\n",
            "Recommended users for skill 'PHP':\n",
            "   id first_name                    email  gender\n",
            "9  10    Rebecka  rannett9@shareasale.com  Female\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert skills to binary representation\n",
        "mlb = MultiLabelBinarizer()\n",
        "skills_binary = mlb.fit_transform(df['Skills'])\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(skills_binary, df['id'])\n",
        "\n",
        "# Function to recommend users based on the input skills\n",
        "def recommend_users_for_skills(skills):\n",
        "    recommended_users = []\n",
        "    skill_binary = mlb.transform([skills])\n",
        "    predicted_users = clf.predict(skill_binary)\n",
        "    recommended_users.extend(predicted_users)\n",
        "    return recommended_users\n",
        "\n",
        "# Get user input for skills\n",
        "selected_skills = input(\"Select your skills (comma-separated): \").split(',')\n",
        "\n",
        "# Get recommended users for the selected skills\n",
        "recommended_users_info = []\n",
        "for skill in selected_skills:\n",
        "    recommended_user_ids = recommend_users_for_skills(skill)\n",
        "    recommended_users_info.append(df[df['id'].isin(recommended_user_ids)][['id', 'first_name', 'email', 'gender']])\n",
        "\n",
        "# Print recommended users for each skill\n",
        "for i, skill in enumerate(selected_skills):\n",
        "    recommended_users = recommended_users_info[i]\n",
        "    if not recommended_users.empty:\n",
        "        print(f\"Recommended users for skill '{skill}':\")\n",
        "        print(recommended_users)\n",
        "    else:\n",
        "        print(f\"No users found with skill '{skill}'.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-cxFiTweZ2t",
        "outputId": "40668e87-b046-4397-89cf-26957786b19b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select your skills (comma-separated): Robotics\n",
            "Recommended users for skill 'Robotics':\n",
            "   id first_name                  email  gender\n",
            "0   1      Daune  dhellwich0@devhub.com  Female\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN\n"
      ],
      "metadata": {
        "id": "p5NpybCnnM9A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "3gnvawLsmn-P"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "\n",
        "# Create a dictionary to store users indexed by their skills\n",
        "users_by_skill = defaultdict(list)\n",
        "for _, row in df.iterrows():\n",
        "    users_by_skill[row['Skills']].append(row['id'])\n",
        "\n",
        "# Prepare the data for the decision tree classifier\n",
        "X = df['Skills'].values.reshape(-1, 1)  # Feature: Skills\n",
        "y = df['id']  # Target: User ID\n",
        "\n",
        "# One-hot encode the skills\n",
        "encoder = OneHotEncoder(handle_unknown='ignore')  # Handle unknown categories gracefully\n",
        "X_encoded = encoder.fit_transform(X)\n",
        "\n",
        "# Train a decision tree classifier\n",
        "clf = DecisionTreeClassifier()\n",
        "clf.fit(X_encoded, y)\n",
        "\n",
        "# Function to recommend users based on the input skills\n",
        "def recommend_users_for_skills(skills):\n",
        "    # Encode the input skills\n",
        "    skills_encoded = encoder.transform([[skill] for skill in skills])\n",
        "\n",
        "    # Predict users with similar skills using the trained classifier\n",
        "    predicted_users = clf.predict(skills_encoded)\n",
        "\n",
        "    # Get all users with common skills\n",
        "    recommended_user_ids = set()\n",
        "    for user_id in predicted_users:\n",
        "        recommended_user_ids.update(users_by_skill[df.loc[df['id'] == user_id, 'Skills'].values[0]])\n",
        "    return recommended_user_ids\n",
        "\n",
        "# Get user input for skills\n",
        "selected_skills = input(\"Select your skills (comma-separated): \").split(',')\n",
        "\n",
        "# Get recommended users for the selected skills\n",
        "recommended_user_ids = recommend_users_for_skills(selected_skills)\n",
        "recommended_users_info = df[df['id'].isin(recommended_user_ids)][['id', 'first_name', 'email', 'gender']]\n",
        "\n",
        "# Print recommended users\n",
        "if not recommended_users_info.empty:\n",
        "    print(\"Recommended users:\")\n",
        "    print(recommended_users_info)\n",
        "else:\n",
        "    print(\"No users found with the selected skills.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfSXVNaToeXi",
        "outputId": "83d8e86c-500a-43c8-87f1-a7c1226c20e9"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select your skills (comma-separated): PHP\n",
            "Recommended users:\n",
            "      id    first_name                           email       gender\n",
            "21    22        Worthy  wmerryfieldl@blogtalkradio.com         Male\n",
            "93    94         Danit     dnoseworthy2l@bloomberg.com      Agender\n",
            "103  104       Edgardo            eocarney2v@tmall.com         Male\n",
            "108  109        Morgun          mjeffree30@latimes.com         Male\n",
            "109  110          Alie           awolfindale31@icq.com       Female\n",
            "111  112          Bart          bduchesne33@devhub.com         Male\n",
            "135  136         Lewie              llindl3r@google.es         Male\n",
            "151  152       Bernice  bgullyes47@merriam-webster.com       Female\n",
            "178  179       Blancha             bhynes4y@cdbaby.com       Female\n",
            "179  180     Kimberlyn           kguillot4z@discuz.net   Polygender\n",
            "229  230         Delly          ddiver6d@biglobe.ne.jp       Female\n",
            "260  261  Bartholomeus         brabbitts78@4shared.com         Male\n",
            "273  274       Leodora            lrein7l@illinois.edu       Female\n",
            "286  287         Jacki          jcastelain7y@house.gov   Polygender\n",
            "295  296      Sharline             sdaleman87@narod.ru       Female\n",
            "312  313       Natalya            ncoveny8o@ustream.tv       Female\n",
            "324  325     Madelaine            mcoucher90@nymag.com       Female\n",
            "347  348      Mechelle            molennane9n@nasa.gov       Female\n",
            "370  371         Carma               cfazanaa@furl.net       Female\n",
            "393  394        Calida            cweadickax@zdnet.com       Female\n",
            "430  431        Darice              dalfordby@java.com       Female\n",
            "441  442    Vilhelmina       vsimonitec9@tuttocitta.it       Female\n",
            "461  462       Geralda            gsimoct@usatoday.com       Female\n",
            "509  510       Ferrell            fmettricke5@yale.edu         Male\n",
            "558  559        Lavena          lmurrhauptfi@umich.edu       Female\n",
            "575  576         Allie           apatersonfz@google.pl       Female\n",
            "596  597          Lyon               lpirsongk@free.fr         Male\n",
            "612  613        Jaimie          jnestleh0@omniture.com         Male\n",
            "613  614       Marlene               mrotteryh1@un.org       Female\n",
            "622  623         Ewell       edunphieha@guardian.co.uk         Male\n",
            "636  637       Madelyn             mesherwoodho@qq.com       Female\n",
            "637  638         Julia   jtownshendhp@businesswire.com       Female\n",
            "640  641      Papagena  pblenkinshiphs@sourceforge.net       Female\n",
            "643  644       Salomon          shearnehv@berkeley.edu         Male\n",
            "670  671       Marketa             mioriim@mozilla.com       Female\n",
            "678  679       Westley             wdoveiu@archive.org         Male\n",
            "688  689        Bondon          bheddej4@instagram.com         Male\n",
            "762  763         Paola          probilliardl6@usda.gov       Female\n",
            "794  795        Clevie        cgalileem2@sitemeter.com         Male\n",
            "875  876       Madalyn             murlingob@google.nl       Female\n",
            "910  911      Gayelord             ghealypa@google.com         Male\n",
            "968  969          Jada      jcoppockqw@fastcompany.com  Genderqueer\n",
            "972  973     Dominique           drighyr0@netvibes.com      Agender\n",
            "991  992     Hephzibah         hmcgingrj@geocities.com       Female\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install flask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0Pm4vctsyw4",
        "outputId": "5de5181c-4daa-4cf9-8ec1-e92aa475ffc9"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: flask in /usr/local/lib/python3.10/dist-packages (2.2.5)\n",
            "Requirement already satisfied: Werkzeug>=2.2.2 in /usr/local/lib/python3.10/dist-packages (from flask) (3.0.1)\n",
            "Requirement already satisfied: Jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from flask) (3.1.3)\n",
            "Requirement already satisfied: itsdangerous>=2.0 in /usr/local/lib/python3.10/dist-packages (from flask) (2.1.2)\n",
            "Requirement already satisfied: click>=8.0 in /usr/local/lib/python3.10/dist-packages (from flask) (8.1.7)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.0->flask) (2.1.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from flask import Flask, render_template, request\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from collections import defaultdict\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "wliTZ5N4tCcu"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7mlHz3OtGxQ",
        "outputId": "ae87eb7f-48de-449a-ae32-171b0542f5c3"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Select a skill from the following options (comma-separated): PHP\n",
            "Recommended users with skill PHP: \n",
            "    id Skills\n",
            "21  22    PHP\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K-VaLOI2tH2x"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}