{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IZfnA6W_GDyf"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MV-7idG1F_NU"
      },
      "outputs": [],
      "source": [
        "# Mock data creation\n",
        "def create_mock_data():\n",
        "    users_data = \"/content/sample_data/1st_train.csv\"\n",
        "    # \"/content/sample_data/train_train.csv\"\n",
        "    applicants = pd.read_csv(users_data)\n",
        "\n",
        "    jobs_data = \"/content/sample_data/jobs_data.csv\"\n",
        "    companies = pd.read_csv(jobs_data)\n",
        "\n",
        "    train_applicants = applicants\n",
        "    test_data = \"/content/sample_data/1st_test.csv\"\n",
        "    # \"/content/sample_data/test_train.csv\"\n",
        "    test_applicants = pd.read_csv(test_data)\n",
        "\n",
        "    return train_applicants, test_applicants, companies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wF1oZ6Ez96BE"
      },
      "outputs": [],
      "source": [
        "train_user, test_user, jobs = create_mock_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gj8tJNrph8Go",
        "outputId": "8e8c625f-3ef1-49b7-f19d-2b70a1c7257f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training data size: 18979\n",
            "Test data size: 4745\n"
          ]
        }
      ],
      "source": [
        "print(\"Training data size:\", train_user.shape[0])\n",
        "print(\"Test data size:\", test_user.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d0XY4al7K0UT"
      },
      "outputs": [],
      "source": [
        "list_hard_skill = [test_user[\"hard_skill\"].iloc[i].replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") for i in range(len(test_user))]\n",
        "list_soft_skill = [test_user[\"soft_skill\"].iloc[i].replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") for i in range(len(test_user))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "JOZ9_NlLK8uS",
        "outputId": "d50e58f3-a1a7-4353-cf39-a39ba354e9d7"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"test_user\",\n  \"rows\": 4745,\n  \"fields\": [\n    {\n      \"column\": \"User ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6816,\n        \"min\": 2,\n        \"max\": 23721,\n        \"num_unique_values\": 4745,\n        \"samples\": [\n          270,\n          1313,\n          19035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"candidate_field\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"retail & consumer products\",\n          \"it jobs\",\n          \"sport & recreation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hard_skill\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4674,\n        \"samples\": [\n          \"['business', 'sales', 'service', 'warehouse operations', 'warehousing', 'excel']\",\n          \"['application', 'business', 'c', 'classified information', 'clojure', 'continuous monitoring', 'debugging', 'ipv4', 'ipv6', 'java', 'javascript', 'kerberos', 'linux', 'linux servers', 'mysql', 'networking', 'operating systems', 'perl', 'php', 'postgresql', 'python', 'relational databases', 'ruby', 'scripting', 'security', 'service', 'software development', 'software development life cycle', 'system', 'system administration', 'tcpdump', 'testing', 'unix', 'web services', 'workday', 'databases']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"soft_skill\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2730,\n        \"samples\": [\n          \"['consulting', 'writing']\",\n          \"['caring nature', 'first aid', 'management', 'planning']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"final_hard_skill\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4674,\n        \"samples\": [\n          \"business, sales, service, warehouse operations, warehousing, excel\",\n          \"application, business, c, classified information, clojure, continuous monitoring, debugging, ipv4, ipv6, java, javascript, kerberos, linux, linux servers, mysql, networking, operating systems, perl, php, postgresql, python, relational databases, ruby, scripting, security, service, software development, software development life cycle, system, system administration, tcpdump, testing, unix, web services, workday, databases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"final_soft_skill\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2730,\n        \"samples\": [\n          \"consulting, writing\",\n          \"caring nature, first aid, management, planning\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "test_user"
            },
            "text/html": [
              "\n",
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              "    <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>User ID</th>\n",
              "      <th>candidate_field</th>\n",
              "      <th>label</th>\n",
              "      <th>hard_skill</th>\n",
              "      <th>soft_skill</th>\n",
              "      <th>final_hard_skill</th>\n",
              "      <th>final_soft_skill</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14649</td>\n",
              "      <td>it jobs</td>\n",
              "      <td>1</td>\n",
              "      <td>['act', 'advertising sales', 'algorithms', 'bu...</td>\n",
              "      <td>['collaboration', 'decision making', 'operatio...</td>\n",
              "      <td>act, advertising sales, algorithms, business, ...</td>\n",
              "      <td>collaboration, decision making, operations, wr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>801</td>\n",
              "      <td>marketing</td>\n",
              "      <td>0</td>\n",
              "      <td>['act', 'brand communication', 'business', 'bu...</td>\n",
              "      <td>['collaboration', 'customer service', 'managem...</td>\n",
              "      <td>act, brand communication, business, business d...</td>\n",
              "      <td>collaboration, customer service, management</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4393</td>\n",
              "      <td>accounting</td>\n",
              "      <td>0</td>\n",
              "      <td>['application', 'balance sheet', 'finance', 'p...</td>\n",
              "      <td>['filing', 'management']</td>\n",
              "      <td>application, balance sheet, finance, property ...</td>\n",
              "      <td>filing, management</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ba90af1-3271-468f-9df2-1db884b8b3b8')\"\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",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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-0ba90af1-3271-468f-9df2-1db884b8b3b8 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-0ba90af1-3271-468f-9df2-1db884b8b3b8');\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-11269c5d-ada6-445b-a4a7-25f488670a39\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11269c5d-ada6-445b-a4a7-25f488670a39')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --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",
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              "      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-11269c5d-ada6-445b-a4a7-25f488670a39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   User ID candidate_field  label  \\\n",
              "0    14649         it jobs      1   \n",
              "1      801       marketing      0   \n",
              "2     4393      accounting      0   \n",
              "\n",
              "                                          hard_skill  \\\n",
              "0  ['act', 'advertising sales', 'algorithms', 'bu...   \n",
              "1  ['act', 'brand communication', 'business', 'bu...   \n",
              "2  ['application', 'balance sheet', 'finance', 'p...   \n",
              "\n",
              "                                          soft_skill  \\\n",
              "0  ['collaboration', 'decision making', 'operatio...   \n",
              "1  ['collaboration', 'customer service', 'managem...   \n",
              "2                           ['filing', 'management']   \n",
              "\n",
              "                                    final_hard_skill  \\\n",
              "0  act, advertising sales, algorithms, business, ...   \n",
              "1  act, brand communication, business, business d...   \n",
              "2  application, balance sheet, finance, property ...   \n",
              "\n",
              "                                    final_soft_skill  \n",
              "0  collaboration, decision making, operations, wr...  \n",
              "1        collaboration, customer service, management  \n",
              "2                                 filing, management  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_user[\"final_hard_skill\"] = pd.DataFrame(list_hard_skill)\n",
        "test_user[\"final_soft_skill\"] = pd.DataFrame(list_soft_skill)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kYbjYsDjABda"
      },
      "outputs": [],
      "source": [
        "list_hard_skill = [train_user[\"hard_skill\"].iloc[i].replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") for i in range(len(train_user))]\n",
        "list_soft_skill = [train_user[\"soft_skill\"].iloc[i].replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") for i in range(len(train_user))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "GC8bn3cjB8D5",
        "outputId": "9c3841bf-a4b1-4003-db83-4ce5a1e3c1be"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "        const element = document.querySelector('#df-18bb0115-79b3-4431-b608-3ac5e3022d7e');\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "\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",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   User ID                  candidate_field  label  \\\n",
              "0     3030                            sales      0   \n",
              "1     9702  administration & office support      0   \n",
              "2     8606       retail & consumer products      0   \n",
              "\n",
              "                                          hard_skill  \\\n",
              "0  ['blogs', 'business', 'lead generation', 'mark...   \n",
              "1  ['business', 'draft', 'go', 'manufacturing', '...   \n",
              "2  ['gross profit', 'inventory', 'inventory manag...   \n",
              "\n",
              "                                          soft_skill  \\\n",
              "0  ['customer service', 'driven personality', 'ma...   \n",
              "1  ['business acumen', 'communications', 'managem...   \n",
              "2                 ['customer service', 'management']   \n",
              "\n",
              "                                    final_hard_skill  \\\n",
              "0  blogs, business, lead generation, marketing st...   \n",
              "1  business, draft, go, manufacturing, office man...   \n",
              "2  gross profit, inventory, inventory management,...   \n",
              "\n",
              "                                    final_soft_skill  \n",
              "0  customer service, driven personality, manageme...  \n",
              "1  business acumen, communications, management, o...  \n",
              "2                       customer service, management  "
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_user[\"final_hard_skill\"] = pd.DataFrame(list_hard_skill)\n",
        "train_user[\"final_soft_skill\"] = pd.DataFrame(list_soft_skill)\n",
        "train_user.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znBy9q8XDcM7"
      },
      "outputs": [],
      "source": [
        "list_hard_skill = [jobs[\"Hard Skills\"].iloc[i].replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") for i in range(len(jobs))]\n",
        "list_soft_skill = [jobs[\"Soft Skills\"].iloc[i].replace(\"[\", \"\").replace(\"]\", \"\").replace(\"'\", \"\") for i in range(len(jobs))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "knFii8o3EQmv",
        "outputId": "eb04651a-e4b8-4859-dad9-c83006902992"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "type": "dataframe",
              "variable_name": "jobs"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-0c84c108-36cf-4f5b-b3a3-9fd6388d642c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Job ID</th>\n",
              "      <th>Major</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>administration &amp; office support</td>\n",
              "      <td>['service', 'business', 'data', 'excel', 'appl...</td>\n",
              "      <td>['management', 'customer service', 'microsoft ...</td>\n",
              "      <td>service, business, data, excel, application, s...</td>\n",
              "      <td>management, customer service, microsoft office...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>advertising, arts &amp; media</td>\n",
              "      <td>['business', 'digital', 'sales', 'service', 'a...</td>\n",
              "      <td>['management', 'social media', 'writing', 'com...</td>\n",
              "      <td>business, digital, sales, service, application...</td>\n",
              "      <td>management, social media, writing, communicati...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c84c108-36cf-4f5b-b3a3-9fd6388d642c')\"\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-0c84c108-36cf-4f5b-b3a3-9fd6388d642c 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-0c84c108-36cf-4f5b-b3a3-9fd6388d642c');\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-8fa274a7-609d-4018-b137-79efc08383ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8fa274a7-609d-4018-b137-79efc08383ee')\"\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-8fa274a7-609d-4018-b137-79efc08383ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Job ID                            Major  \\\n",
              "0       1                       accounting   \n",
              "1       2  administration & office support   \n",
              "2       3        advertising, arts & media   \n",
              "\n",
              "                                         Hard Skills  \\\n",
              "0  ['business', 'finance', 'excel', 'tax', 'servi...   \n",
              "1  ['service', 'business', 'data', 'excel', 'appl...   \n",
              "2  ['business', 'digital', 'sales', 'service', 'a...   \n",
              "\n",
              "                                         Soft Skills  \\\n",
              "0  ['management', 'planning', 'operations', 'lead...   \n",
              "1  ['management', 'customer service', 'microsoft ...   \n",
              "2  ['management', 'social media', 'writing', 'com...   \n",
              "\n",
              "                                    final_hard_skill  \\\n",
              "0  business, finance, excel, tax, service, data, ...   \n",
              "1  service, business, data, excel, application, s...   \n",
              "2  business, digital, sales, service, application...   \n",
              "\n",
              "                                    final_soft_skill  \n",
              "0  management, planning, operations, leadership, ...  \n",
              "1  management, customer service, microsoft office...  \n",
              "2  management, social media, writing, communicati...  "
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "jobs[\"final_hard_skill\"] = pd.DataFrame(list_hard_skill)\n",
        "jobs[\"final_soft_skill\"] = pd.DataFrame(list_soft_skill)\n",
        "jobs.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wiDiHL6lStnd"
      },
      "outputs": [],
      "source": [
        "# Feature Engineering\n",
        "def feature_engineering(applicants, companies):\n",
        "    # Vectorize skills and majors\n",
        "    tfidf_vectorizer_skills = TfidfVectorizer()\n",
        "    tfidf_vectorizer_majors = TfidfVectorizer()\n",
        "\n",
        "    all_skills = pd.concat([applicants['final_hard_skill'], applicants['final_soft_skill'],\n",
        "                            companies['final_hard_skill'], companies['final_soft_skill']])\n",
        "    all_majors = pd.concat([applicants['candidate_field'], companies['Major']])\n",
        "\n",
        "    all_skills_vectorized = tfidf_vectorizer_skills.fit_transform(all_skills)\n",
        "    all_majors_vectorized = tfidf_vectorizer_majors.fit_transform(all_majors)\n",
        "\n",
        "    num_applicants = len(applicants)\n",
        "    num_companies = len(companies)\n",
        "\n",
        "    # Split the TF-IDF vectors back into applicants and companies\n",
        "    applicants_skills_vectorized = all_skills_vectorized[:num_applicants*2]  # because each applicant has 2 skill entries\n",
        "    companies_skills_vectorized = all_skills_vectorized[num_applicants*2:]\n",
        "\n",
        "    applicants_majors_vectorized = all_majors_vectorized[:num_applicants]\n",
        "    companies_majors_vectorized = all_majors_vectorized[num_applicants:]\n",
        "\n",
        "    return (applicants_skills_vectorized, applicants_majors_vectorized,\n",
        "            companies_skills_vectorized, companies_majors_vectorized)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "THM0mszQGNyD"
      },
      "outputs": [],
      "source": [
        "def compute_similarity(applicants_skills_vectorized, applicants_majors_vectorized,\n",
        "                       companies_skills_vectorized, companies_majors_vectorized):\n",
        "    # Calculate similarity based on skills (averaging hard and soft skills similarities)\n",
        "    applicants_skills = (applicants_skills_vectorized[0::2] + applicants_skills_vectorized[1::2]) / 2\n",
        "    companies_skills = (companies_skills_vectorized[0::2] + companies_skills_vectorized[1::2]) / 2\n",
        "\n",
        "    skills_similarity = cosine_similarity(applicants_skills, companies_skills)\n",
        "\n",
        "    # Calculate similarity based on majors\n",
        "    majors_similarity = cosine_similarity(applicants_majors_vectorized, companies_majors_vectorized)\n",
        "\n",
        "    # Ensure the number of companies in both similarities is aligned\n",
        "    if skills_similarity.shape[1] != majors_similarity.shape[1]:\n",
        "        min_dim = min(skills_similarity.shape[1], majors_similarity.shape[1])\n",
        "        skills_similarity = skills_similarity[:, :min_dim]\n",
        "        majors_similarity = majors_similarity[:, :min_dim]\n",
        "\n",
        "    # Combine these similarities (simple average for this example)\n",
        "    combined_similarity = (skills_similarity + majors_similarity) / 2\n",
        "    return combined_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ter3YAzxoelD"
      },
      "outputs": [],
      "source": [
        "# Recommendation Function\n",
        "def recommend_jobs(applicants, companies, similarity_scores):\n",
        "    recommendations = {}\n",
        "    for i, applicant in enumerate(applicants['User ID']):\n",
        "        if i < len(similarity_scores):\n",
        "            sorted_company_indices = np.argsort(-similarity_scores[i])  # Descending sort of scores\n",
        "            recommended_companies = companies.iloc[sorted_company_indices]['Major'].values[:3]  # Top 3 recommendations\n",
        "            recommendations[applicant] = recommended_companies\n",
        "    return recommendations\n",
        "\n",
        "# Testing and Evaluation Function\n",
        "def print_recommendations(applicants, companies, recommendations):\n",
        "    # This is a mock function since we don't have ground truth to compare to.\n",
        "    # In a real scenario, we would compare against actual matches or use some form of feedback.\n",
        "    print(\"Recommendations for each applicant:\")\n",
        "    for applicant in recommendations:\n",
        "        print(f\"{applicant}: {recommendations[applicant]}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Ajxp0xelIrl2",
        "outputId": "64d4d8e9-e002-413d-949f-9ef178ced2a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "5046: ['marketing' 'advertising, arts & media' 'administration & office support']\n",
            "2043: ['accounting' 'advertising, arts & media' 'marketing']\n",
            "3611: ['accounting' 'advertising, arts & media' 'marketing']\n",
            "6712: ['it jobs' 'healthcare & medical' 'recruitment consultancy jobs']\n",
            "17086: ['recruitment consultancy jobs' 'it jobs' 'healthcare & medical']\n",
            "20679: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "16506: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n",
            "324: ['sales' 'accounting' 'marketing']\n",
            "21541: ['accounting' 'advertising, arts & media' 'marketing']\n",
            "5183: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n",
            "4234: ['recruitment consultancy jobs' 'it jobs' 'marketing']\n",
            "22070: ['advertising, arts & media' 'marketing' 'healthcare & medical']\n",
            "11773: ['it jobs' 'healthcare & medical' 'recruitment consultancy jobs']\n",
            "8820: ['administration & office support' 'accounting'\n",
            " 'advertising, arts & media']\n",
            "14512: ['it jobs' 'healthcare & medical' 'administration & office support']\n",
            "22176: ['it jobs' 'healthcare & medical' 'recruitment consultancy jobs']\n",
            "87: ['it jobs' 'administration & office support'\n",
            " 'recruitment consultancy jobs']\n",
            "11725: ['it jobs' 'administration & office support' 'marketing']\n",
            "14937: ['it jobs' 'accounting' 'recruitment consultancy jobs']\n",
            "282: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "5454: ['accounting' 'administration & office support' 'marketing']\n",
            "21343: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "23558: ['it jobs' 'advertising, arts & media' 'recruitment consultancy jobs']\n",
            "7773: ['sport & recreation' 'accounting' 'advertising, arts & media']\n",
            "2163: ['it jobs' 'healthcare & medical' 'recruitment consultancy jobs']\n",
            "11386: ['it jobs' 'healthcare & medical' 'administration & office support']\n",
            "5273: ['healthcare & medical' 'it jobs' 'accounting']\n",
            "14138: ['it jobs' 'recruitment consultancy jobs' 'healthcare & medical']\n",
            "14068: ['sales' 'accounting' 'it jobs']\n",
            "18228: ['administration & office support' 'advertising, arts & media' 'marketing']\n",
            "13829: ['sales' 'administration & office support' 'it jobs']\n",
            "1864: ['healthcare & medical' 'marketing' 'advertising, arts & media']\n",
            "20513: ['recruitment consultancy jobs' 'it jobs' 'marketing']\n",
            "9661: ['it jobs' 'advertising, arts & media' 'recruitment consultancy jobs']\n",
            "8045: ['administration & office support' 'advertising, arts & media'\n",
            " 'healthcare & medical']\n",
            "440: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "18359: ['administration & office support' 'advertising, arts & media' 'marketing']\n",
            "1248: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n",
            "17209: ['healthcare & medical' 'accounting' 'it jobs']\n",
            "10487: ['it jobs' 'marketing' 'accounting']\n",
            "23666: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "6955: ['administration & office support' 'advertising, arts & media' 'marketing']\n",
            "5138: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "8132: ['healthcare & medical' 'advertising, arts & media' 'it jobs']\n",
            "6026: ['marketing' 'advertising, arts & media' 'it jobs']\n",
            "10788: ['administration & office support' 'it jobs' 'accounting']\n",
            "16080: ['healthcare & medical' 'it jobs' 'accounting']\n",
            "4213: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "19120: ['retail & consumer products' 'advertising, arts & media' 'marketing']\n",
            "15202: ['advertising, arts & media' 'administration & office support' 'it jobs']\n",
            "23140: ['it jobs' 'recruitment consultancy jobs' 'sport & recreation']\n",
            "4026: ['it jobs' 'accounting' 'recruitment consultancy jobs']\n",
            "18858: ['it jobs' 'administration & office support'\n",
            " 'recruitment consultancy jobs']\n",
            "20919: ['healthcare & medical' 'advertising, arts & media' 'marketing']\n",
            "21657: ['healthcare & medical' 'it jobs' 'marketing']\n",
            "16283: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "1945: ['it jobs' 'accounting' 'recruitment consultancy jobs']\n",
            "171: ['it jobs' 'advertising, arts & media' 'recruitment consultancy jobs']\n",
            "7297: ['it jobs' 'administration & office support'\n",
            " 'recruitment consultancy jobs']\n",
            "18071: ['it jobs' 'healthcare & medical' 'recruitment consultancy jobs']\n",
            "9137: ['marketing' 'advertising, arts & media' 'administration & office support']\n",
            "5069: ['it jobs' 'recruitment consultancy jobs'\n",
            " 'administration & office support']\n",
            "12894: ['accounting' 'advertising, arts & media'\n",
            " 'administration & office support']\n",
            "23499: ['accounting' 'healthcare & medical' 'advertising, arts & media']\n",
            "5542: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n",
            "16307: ['it jobs' 'administration & office support' 'healthcare & medical']\n",
            "13611: ['advertising, arts & media' 'marketing' 'administration & office support']\n",
            "9244: ['it jobs' 'advertising, arts & media' 'recruitment consultancy jobs']\n",
            "16559: ['it jobs' 'administration & office support' 'marketing']\n",
            "10799: ['it jobs' 'recruitment consultancy jobs' 'advertising, arts & media']\n",
            "10532: ['it jobs' 'recruitment consultancy jobs' 'sport & recreation']\n",
            "20920: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n",
            "10305: ['accounting' 'marketing' 'advertising, arts & media']\n",
            "16414: ['accounting' 'advertising, arts & media' 'marketing']\n",
            "680: ['administration & office support' 'marketing' 'advertising, arts & media']\n",
            "6709: ['healthcare & medical' 'advertising, arts & media' 'marketing']\n",
            "8722: ['retail & consumer products' 'healthcare & medical' 'it jobs']\n",
            "18113: ['it jobs' 'healthcare & medical' 'recruitment consultancy jobs']\n",
            "11683: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n",
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            "19190: ['it jobs' 'recruitment consultancy jobs' 'marketing']\n"
          ]
        }
      ],
      "source": [
        "# Let's create and process the data, and compute recommendations\n",
        "# train_applicants, test_applicants, companies = create_mock_data()\n",
        "applicants_skills_vec, applicants_majors_vec, companies_skills_vec, companies_majors_vec = feature_engineering(train_user, jobs)\n",
        "similarity_scores = compute_similarity(applicants_skills_vec, applicants_majors_vec, companies_skills_vec, companies_majors_vec)\n",
        "recommendations = recommend_jobs(test_user, jobs, similarity_scores)\n",
        "\n",
        "# Output the recommendations to observe the results\n",
        "print_recommendations(test_user, jobs, recommendations)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nj-HEdyJlYNY"
      },
      "outputs": [],
      "source": [
        "# Process input skills and recommend jobs\n",
        "def recommend_jobs_for_input_skills(input_hard_skills, input_soft_skills, input_major, companies, tfidf_vectorizer_skills, tfidf_vectorizer_majors, companies_skills_vec, companies_majors_vec):\n",
        "    input_hard_skills_vec = tfidf_vectorizer_skills.transform([input_hard_skills])\n",
        "    input_soft_skills_vec = tfidf_vectorizer_skills.transform([input_soft_skills])\n",
        "    input_major_vec = tfidf_vectorizer_majors.transform([input_major])\n",
        "\n",
        "    # Average the vectorized hard and soft skills\n",
        "    input_skills_vec = (input_hard_skills_vec + input_soft_skills_vec) / 2\n",
        "\n",
        "    # Compute similarities\n",
        "    skills_similarity = cosine_similarity(input_skills_vec, companies_skills_vec)\n",
        "    major_similarity = cosine_similarity(input_major_vec, companies_majors_vec)\n",
        "\n",
        "    # Ensure the number of companies in both similarities is aligned\n",
        "    if skills_similarity.shape[1] != major_similarity.shape[1]:\n",
        "        min_dim = min(skills_similarity.shape[1], major_similarity.shape[1])\n",
        "        skills_similarity = skills_similarity[:, :min_dim]\n",
        "        major_similarity = major_similarity[:, :min_dim]\n",
        "\n",
        "    # Combine similarities\n",
        "    combined_similarity = (skills_similarity + major_similarity) / 2\n",
        "\n",
        "    # Get top 3 job recommendations\n",
        "    sorted_company_indices = np.argsort(-combined_similarity[0])\n",
        "    recommended_companies = companies.iloc[sorted_company_indices]['Major'].values[:3]\n",
        "\n",
        "    return recommended_companies\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ok_-ol5q3Gt",
        "outputId": "b19cd645-22f4-4ef0-db51-a7dd178f985e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recommended Jobs based on input skills and major:\n",
            "['marketing' 'sales' 'administration & office support']\n"
          ]
        }
      ],
      "source": [
        "input_hard_skills = \"Business, Finance, Excel\"\n",
        "input_soft_skills = \"Communication, Teamwork\"\n",
        "input_major = \"Marketing\"\n",
        "\n",
        "recommended_jobs = recommend_jobs_for_input_skills(input_hard_skills, input_soft_skills, input_major, jobs, tfidf_vectorizer_skills, tfidf_vectorizer_majors, companies_skills_vec, companies_majors_vec)\n",
        "print(\"Recommended Jobs based on input skills and major:\")\n",
        "print(recommended_jobs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kShd99z_NiTa"
      },
      "source": [
        "Evaluating (PENDING)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WfEgjqw9JE3l"
      },
      "outputs": [],
      "source": [
        "def create_ground_truth(csv_file_path):\n",
        "    data = pd.read_csv(csv_file_path)\n",
        "\n",
        "    # Tạo dictionary `ground_truth`\n",
        "    ground_truth = {}\n",
        "    for index, row in data.iterrows():\n",
        "        user_id = row['User ID']\n",
        "        actual_major = row['candidate_field']\n",
        "\n",
        "        # Thêm vào dictionary, giả sử mỗi ứng viên chỉ chọn một công việc\n",
        "        ground_truth[user_id] = [actual_major]\n",
        "\n",
        "    return ground_truth\n",
        "\n",
        "# Sử dụng hàm trên để tạo `ground_truth`\n",
        "csv_file_path = '/content/sample_data/1st_test.csv'\n",
        "ground_truth = create_ground_truth(csv_file_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "TRiD4oS-AKFE",
        "outputId": "256fadeb-b250-4602-affb-005cb9c658eb"
      },
      "outputs": [
        {
          "data": {
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              " 7676: ['telecommunication'],\n",
              " 15924: ['it jobs'],\n",
              " 9043: ['administration & office support'],\n",
              " 6824: ['retail & consumer products'],\n",
              " 7951: ['healthcare & medical'],\n",
              " 13144: ['retail & consumer products'],\n",
              " 8301: ['telecommunication'],\n",
              " 10481: ['accounting'],\n",
              " 15807: ['it jobs'],\n",
              " 6655: ['telecommunication'],\n",
              " 7091: ['retail & consumer products'],\n",
              " 14168: ['accounting'],\n",
              " 20239: ['it jobs'],\n",
              " 1701: ['marketing'],\n",
              " 23475: ['it jobs'],\n",
              " 21104: ['it jobs'],\n",
              " 10396: ['sales'],\n",
              " 19046: ['it jobs'],\n",
              " 5322: ['retail & consumer products'],\n",
              " 16154: ['it jobs'],\n",
              " 19103: ['it jobs'],\n",
              " 11144: ['marketing'],\n",
              " 9795: ['healthcare & medical'],\n",
              " 8268: ['marketing'],\n",
              " 14442: ['sales'],\n",
              " 1365: ['telecommunication'],\n",
              " 2213: ['sales'],\n",
              " 12651: ['sales'],\n",
              " 10825: ['recruitment consultancy jobs'],\n",
              " 15381: ['it jobs'],\n",
              " 458: ['healthcare & medical'],\n",
              " 17938: ['it jobs'],\n",
              " 20964: ['it jobs'],\n",
              " 2358: ['marketing'],\n",
              " 16366: ['it jobs'],\n",
              " 19866: ['it jobs'],\n",
              " 18775: ['it jobs'],\n",
              " 15415: ['it jobs'],\n",
              " 14919: ['it jobs'],\n",
              " 19019: ['it jobs'],\n",
              " 9907: ['telecommunication'],\n",
              " 8366: ['healthcare & medical'],\n",
              " 18032: ['it jobs'],\n",
              " 20632: ['it jobs'],\n",
              " 17432: ['it jobs'],\n",
              " 16662: ['it jobs'],\n",
              " 3341: ['telecommunication'],\n",
              " 13327: ['marketing'],\n",
              " 4791: ['sales'],\n",
              " 197: ['accounting'],\n",
              " 21654: ['it jobs'],\n",
              " 1728: ['telecommunication'],\n",
              " 14341: ['sales'],\n",
              " 9575: ['sales'],\n",
              " 108: ['sales'],\n",
              " 13834: ['recruitment consultancy jobs'],\n",
              " 787: ['administration & office support'],\n",
              " 15647: ['it jobs'],\n",
              " 157: ['accounting'],\n",
              " 20937: ['it jobs'],\n",
              " 10221: ['advertising, arts & media'],\n",
              " 5545: ['telecommunication'],\n",
              " 14166: ['accounting'],\n",
              " 7530: ['administration & office support'],\n",
              " 6674: ['telecommunication'],\n",
              " 13053: ['marketing'],\n",
              " 8138: ['accounting'],\n",
              " 2877: ['accounting'],\n",
              " 22401: ['it jobs'],\n",
              " 18522: ['it jobs'],\n",
              " 14106: ['sales'],\n",
              " 3145: ['administration & office support'],\n",
              " 22852: ['it jobs'],\n",
              " 2119: ['retail & consumer products'],\n",
              " 14299: ['sales'],\n",
              " 22367: ['it jobs'],\n",
              " 23041: ['it jobs'],\n",
              " 20381: ['it jobs'],\n",
              " 5402: ['accounting'],\n",
              " 3074: ['healthcare & medical'],\n",
              " 23520: ['it jobs'],\n",
              " 14743: ['it jobs'],\n",
              " 21026: ['it jobs'],\n",
              " 21681: ['it jobs'],\n",
              " 6835: ['telecommunication'],\n",
              " 17518: ['it jobs'],\n",
              " 2785: ['telecommunication'],\n",
              " 20038: ['it jobs'],\n",
              " 6326: ['retail & consumer products'],\n",
              " 10753: ['retail & consumer products'],\n",
              " 10804: ['retail & consumer products'],\n",
              " 10675: ['marketing'],\n",
              " 12337: ['it jobs'],\n",
              " 2556: ['healthcare & medical'],\n",
              " 15474: ['it jobs'],\n",
              " 21611: ['it jobs'],\n",
              " 2987: ['telecommunication'],\n",
              " 18645: ['it jobs'],\n",
              " 10544: ['recruitment consultancy jobs'],\n",
              " 11455: ['accounting'],\n",
              " 2150: ['telecommunication'],\n",
              " 7149: ['healthcare & medical'],\n",
              " 1764: ['accounting'],\n",
              " 9580: ['telecommunication'],\n",
              " 5586: ['sales'],\n",
              " 3143: ['retail & consumer products'],\n",
              " 11139: ['marketing'],\n",
              " 12462: ['it jobs'],\n",
              " 10088: ['telecommunication'],\n",
              " 18986: ['it jobs'],\n",
              " 14098: ['accounting'],\n",
              " 2511: ['telecommunication'],\n",
              " 8498: ['retail & consumer products'],\n",
              " 11398: ['accounting'],\n",
              " 21371: ['it jobs'],\n",
              " 23662: ['it jobs'],\n",
              " 15637: ['it jobs'],\n",
              " 18935: ['it jobs'],\n",
              " 4683: ['sport & recreation'],\n",
              " 10798: ['retail & consumer products'],\n",
              " 18754: ['it jobs'],\n",
              " 21936: ['it jobs'],\n",
              " 47: ['telecommunication'],\n",
              " 3525: ['healthcare & medical'],\n",
              " 2129: ['telecommunication'],\n",
              " 13991: ['accounting'],\n",
              " 15785: ['it jobs'],\n",
              " 16789: ['it jobs'],\n",
              " 16564: ['it jobs'],\n",
              " 15035: ['it jobs'],\n",
              " 3055: ['retail & consumer products'],\n",
              " 9052: ['accounting'],\n",
              " 1343: ['administration & office support'],\n",
              " 13155: ['marketing'],\n",
              " 19360: ['it jobs'],\n",
              " 5329: ['healthcare & medical'],\n",
              " 16444: ['it jobs'],\n",
              " 23587: ['it jobs'],\n",
              " 7952: ['telecommunication'],\n",
              " 21565: ['it jobs'],\n",
              " 22579: ['it jobs'],\n",
              " 743: ['healthcare & medical'],\n",
              " 8446: ['telecommunication'],\n",
              " 11437: ['accounting'],\n",
              " 14164: ['accounting'],\n",
              " 20730: ['it jobs'],\n",
              " 11358: ['accounting'],\n",
              " 2939: ['telecommunication'],\n",
              " 18828: ['it jobs'],\n",
              " 9579: ['healthcare & medical'],\n",
              " 20124: ['it jobs'],\n",
              " 13841: ['accounting'],\n",
              " 3778: ['telecommunication'],\n",
              " 20941: ['it jobs'],\n",
              " 5954: ['sales'],\n",
              " 483: ['telecommunication'],\n",
              " 1221: ['healthcare & medical'],\n",
              " 11797: ['recruitment consultancy jobs'],\n",
              " 982: ['healthcare & medical'],\n",
              " 15275: ['it jobs'],\n",
              " 14120: ['accounting'],\n",
              " 20543: ['it jobs'],\n",
              " 22499: ['it jobs'],\n",
              " 11905: ['accounting'],\n",
              " 18457: ['it jobs'],\n",
              " 21077: ['it jobs'],\n",
              " 7049: ['healthcare & medical'],\n",
              " 12924: ['recruitment consultancy jobs'],\n",
              " 12328: ['sales'],\n",
              " 22221: ['it jobs'],\n",
              " 9343: ['telecommunication'],\n",
              " 15492: ['it jobs'],\n",
              " 14439: ['sales'],\n",
              " 20017: ['it jobs'],\n",
              " 11479: ['accounting'],\n",
              " 22417: ['it jobs'],\n",
              " 6606: ['accounting'],\n",
              " 23517: ['it jobs'],\n",
              " 9142: ['healthcare & medical'],\n",
              " 21477: ['it jobs'],\n",
              " 4240: ['accounting'],\n",
              " 12978: ['recruitment consultancy jobs'],\n",
              " 11930: ['accounting'],\n",
              " 17441: ['it jobs'],\n",
              " 10016: ['telecommunication'],\n",
              " 7625: ['administration & office support'],\n",
              " 5351: ['telecommunication'],\n",
              " 12712: ['sales'],\n",
              " 15451: ['it jobs'],\n",
              " 12152: ['it jobs'],\n",
              " 19506: ['it jobs'],\n",
              " 3783: ['marketing'],\n",
              " 19394: ['it jobs'],\n",
              " 13816: ['accounting'],\n",
              " 7632: ['sales'],\n",
              " 10412: ['retail & consumer products'],\n",
              " 8223: ['telecommunication'],\n",
              " 8042: ['sales'],\n",
              " 15456: ['it jobs'],\n",
              " 1503: ['accounting'],\n",
              " 8079: ['healthcare & medical'],\n",
              " 2212: ['administration & office support'],\n",
              " 8733: ['healthcare & medical'],\n",
              " 18275: ['it jobs'],\n",
              " 11690: ['recruitment consultancy jobs'],\n",
              " 16783: ['it jobs'],\n",
              " 201: ['healthcare & medical'],\n",
              " 76: ['healthcare & medical'],\n",
              " 10120: ['telecommunication'],\n",
              " 1841: ['retail & consumer products'],\n",
              " 8818: ['healthcare & medical'],\n",
              " 16785: ['it jobs'],\n",
              " 3755: ['marketing'],\n",
              " 16432: ['it jobs'],\n",
              " 11861: ['recruitment consultancy jobs'],\n",
              " 21885: ['it jobs'],\n",
              " 18751: ['it jobs'],\n",
              " 213: ['retail & consumer products'],\n",
              " 21792: ['it jobs'],\n",
              " 14019: ['accounting'],\n",
              " 11409: ['accounting'],\n",
              " 9507: ['healthcare & medical'],\n",
              " 10902: ['recruitment consultancy jobs'],\n",
              " 561: ['sales'],\n",
              " 8748: ['accounting'],\n",
              " 2953: ['sales'],\n",
              " 20980: ['it jobs'],\n",
              " 3352: ['accounting'],\n",
              " 5382: ['telecommunication'],\n",
              " 6958: ['healthcare & medical'],\n",
              " 2166: ['retail & consumer products'],\n",
              " 10559: ['retail & consumer products'],\n",
              " 21072: ['it jobs'],\n",
              " 20905: ['it jobs'],\n",
              " 19205: ['it jobs'],\n",
              " 10157: ['advertising, arts & media'],\n",
              " 495: ['telecommunication'],\n",
              " 18076: ['it jobs'],\n",
              " 14905: ['it jobs'],\n",
              " 5199: ['healthcare & medical'],\n",
              " 2120: ['advertising, arts & media'],\n",
              " 9768: ['healthcare & medical'],\n",
              " 16359: ['it jobs'],\n",
              " 14753: ['it jobs'],\n",
              " 15098: ['it jobs'],\n",
              " 9789: ['telecommunication'],\n",
              " 10308: ['administration & office support'],\n",
              " 22304: ['it jobs'],\n",
              " 22323: ['it jobs'],\n",
              " 10501: ['retail & consumer products'],\n",
              " 6512: ['administration & office support'],\n",
              " 6974: ['administration & office support'],\n",
              " 5480: ['sales'],\n",
              " 17005: ['it jobs'],\n",
              " 20659: ['it jobs'],\n",
              " 13621: ['accounting'],\n",
              " 13466: ['accounting'],\n",
              " 1278: ['administration & office support'],\n",
              " 5811: ['telecommunication'],\n",
              " 10606: ['retail & consumer products'],\n",
              " 5723: ['telecommunication'],\n",
              " 21525: ['it jobs'],\n",
              " 17726: ['it jobs'],\n",
              " 7488: ['retail & consumer products'],\n",
              " 22656: ['it jobs'],\n",
              " 11949: ['accounting'],\n",
              " 21728: ['it jobs'],\n",
              " 1596: ['marketing'],\n",
              " 4990: ['accounting'],\n",
              " 16508: ['it jobs'],\n",
              " 8159: ['marketing'],\n",
              " 6527: ['administration & office support'],\n",
              " 174: ['marketing'],\n",
              " 4982: ['sport & recreation'],\n",
              " 5893: ['administration & office support'],\n",
              " 14553: ['sales'],\n",
              " 17570: ['it jobs'],\n",
              " 20352: ['it jobs'],\n",
              " 8169: ['accounting'],\n",
              " 11293: ['accounting'],\n",
              " 21247: ['it jobs'],\n",
              " 16622: ['it jobs'],\n",
              " 22385: ['it jobs'],\n",
              " 20356: ['it jobs'],\n",
              " 23012: ['it jobs'],\n",
              " 2688: ['telecommunication'],\n",
              " 2524: ['healthcare & medical'],\n",
              " 1543: ['healthcare & medical'],\n",
              " 2270: ['sales'],\n",
              " 4055: ['sales'],\n",
              " 2179: ['accounting'],\n",
              " 8059: ['telecommunication'],\n",
              " 20437: ['it jobs'],\n",
              " 3304: ['healthcare & medical'],\n",
              " 8614: ['sales'],\n",
              " 21682: ['it jobs'],\n",
              " 5017: ['sales'],\n",
              " 769: ['marketing'],\n",
              " 15148: ['it jobs'],\n",
              " 3592: ['sport & recreation'],\n",
              " 14729: ['it jobs'],\n",
              " 13715: ['recruitment consultancy jobs'],\n",
              " 21969: ['it jobs'],\n",
              " 19753: ['it jobs'],\n",
              " 18478: ['it jobs'],\n",
              " 7358: ['administration & office support'],\n",
              " 19475: ['it jobs'],\n",
              " 16280: ['it jobs'],\n",
              " 4065: ['telecommunication'],\n",
              " 18838: ['it jobs'],\n",
              " 20533: ['it jobs'],\n",
              " 14838: ['it jobs'],\n",
              " 22952: ['it jobs'],\n",
              " 21206: ['it jobs'],\n",
              " 23527: ['it jobs'],\n",
              " 21108: ['it jobs'],\n",
              " 16942: ['it jobs'],\n",
              " 16072: ['it jobs'],\n",
              " 20960: ['it jobs'],\n",
              " 2776: ['telecommunication'],\n",
              " 14055: ['sales'],\n",
              " 13131: ['retail & consumer products'],\n",
              " 23020: ['it jobs'],\n",
              " 2850: ['sales'],\n",
              " 10612: ['recruitment consultancy jobs'],\n",
              " 19681: ['it jobs'],\n",
              " 18251: ['it jobs'],\n",
              " 14102: ['accounting'],\n",
              " 9900: ['telecommunication'],\n",
              " 13574: ['accounting'],\n",
              " 11490: ['accounting'],\n",
              " 7195: ['sales'],\n",
              " 19967: ['it jobs'],\n",
              " 11746: ['recruitment consultancy jobs'],\n",
              " 18268: ['it jobs'],\n",
              " 3648: ['accounting'],\n",
              " 915: ['accounting'],\n",
              " 4735: ['sales'],\n",
              " 8273: ['telecommunication'],\n",
              " 10624: ['retail & consumer products'],\n",
              " 15494: ['it jobs'],\n",
              " 4609: ['healthcare & medical'],\n",
              " 4460: ['healthcare & medical'],\n",
              " 18799: ['it jobs'],\n",
              " 7074: ['sales'],\n",
              " 4476: ['telecommunication'],\n",
              " 3362: ['healthcare & medical'],\n",
              " 14332: ['sales'],\n",
              " 11172: ['marketing'],\n",
              " 12926: ['recruitment consultancy jobs'],\n",
              " 17428: ['it jobs'],\n",
              " 17455: ['it jobs'],\n",
              " 16438: ['it jobs'],\n",
              " 14225: ['accounting'],\n",
              " 2: ['sales'],\n",
              " 2923: ['sales'],\n",
              " 12979: ['recruitment consultancy jobs'],\n",
              " 175: ['accounting'],\n",
              " 6765: ['telecommunication'],\n",
              " 12511: ['sales'],\n",
              " 3397: ['sales'],\n",
              " 18994: ['it jobs'],\n",
              " 6219: ['accounting'],\n",
              " 4004: ['sales'],\n",
              " 7305: ['marketing'],\n",
              " 490: ['healthcare & medical'],\n",
              " 2083: ['healthcare & medical'],\n",
              " 5090: ['sales'],\n",
              " 4380: ['sales'],\n",
              " 20078: ['it jobs'],\n",
              " 5759: ['sales'],\n",
              " 5446: ['telecommunication'],\n",
              " 792: ['healthcare & medical'],\n",
              " 13735: ['recruitment consultancy jobs'],\n",
              " 6603: ['telecommunication'],\n",
              " 20966: ['it jobs'],\n",
              " 22730: ['it jobs'],\n",
              " 11404: ['accounting'],\n",
              " 20291: ['it jobs'],\n",
              " 16291: ['it jobs'],\n",
              " 3983: ['administration & office support'],\n",
              " 9409: ['administration & office support'],\n",
              " 20580: ['it jobs'],\n",
              " 20476: ['it jobs'],\n",
              " 9855: ['marketing'],\n",
              " 9246: ['healthcare & medical'],\n",
              " 21249: ['it jobs'],\n",
              " 17729: ['it jobs'],\n",
              " 11023: ['marketing'],\n",
              " 14754: ['it jobs'],\n",
              " 3559: ['healthcare & medical'],\n",
              " 22469: ['it jobs'],\n",
              " 3613: ['accounting'],\n",
              " 19170: ['it jobs'],\n",
              " 10029: ['accounting'],\n",
              " 23286: ['it jobs'],\n",
              " 3264: ['healthcare & medical'],\n",
              " 18704: ['it jobs'],\n",
              " 19949: ['it jobs'],\n",
              " 17948: ['it jobs'],\n",
              " 2058: ['sales'],\n",
              " 22413: ['it jobs'],\n",
              " 15180: ['it jobs'],\n",
              " 2287: ['healthcare & medical'],\n",
              " 4046: ['healthcare & medical'],\n",
              " 6271: ['administration & office support'],\n",
              " 6192: ['marketing'],\n",
              " 21509: ['it jobs'],\n",
              " 13641: ['accounting'],\n",
              " 18957: ['it jobs'],\n",
              " 17852: ['it jobs'],\n",
              " 19860: ['it jobs'],\n",
              " 12568: ['sales'],\n",
              " 8086: ['sales'],\n",
              " 12843: ['recruitment consultancy jobs'],\n",
              " 16985: ['it jobs'],\n",
              " 4267: ['accounting'],\n",
              " 6296: ['accounting'],\n",
              " 21401: ['it jobs'],\n",
              " 6008: ['healthcare & medical'],\n",
              " 23212: ['it jobs'],\n",
              " 19816: ['it jobs'],\n",
              " 19926: ['it jobs'],\n",
              " 8601: ['accounting'],\n",
              " 15479: ['it jobs'],\n",
              " 6878: ['marketing'],\n",
              " 13602: ['accounting'],\n",
              " 3393: ['healthcare & medical'],\n",
              " 18242: ['it jobs'],\n",
              " 10929: ['marketing'],\n",
              " 5221: ['sales'],\n",
              " 6839: ['sales'],\n",
              " 3024: ['retail & consumer products'],\n",
              " 14279: ['it jobs'],\n",
              " 1016: ['administration & office support'],\n",
              " 8244: ['sales'],\n",
              " 7824: ['administration & office support'],\n",
              " 20305: ['it jobs'],\n",
              " 3823: ['administration & office support'],\n",
              " 12206: ['sales'],\n",
              " 4507: ['marketing'],\n",
              " 3198: ['sales'],\n",
              " 1898: ['sales'],\n",
              " 14824: ['it jobs'],\n",
              " 14958: ['it jobs'],\n",
              " 21387: ['it jobs'],\n",
              " 11311: ['accounting'],\n",
              " 7889: ['accounting'],\n",
              " 22540: ['it jobs'],\n",
              " 828: ['administration & office support'],\n",
              " 3712: ['administration & office support'],\n",
              " 23144: ['it jobs'],\n",
              " 10148: ['telecommunication'],\n",
              " 17136: ['it jobs'],\n",
              " 314: ['telecommunication'],\n",
              " 15809: ['it jobs'],\n",
              " 4207: ['accounting'],\n",
              " 13359: ['retail & consumer products'],\n",
              " 17892: ['it jobs'],\n",
              " 22556: ['it jobs'],\n",
              " 296: ['sales'],\n",
              " 8137: ['advertising, arts & media'],\n",
              " 7725: ['healthcare & medical'],\n",
              " 9608: ['administration & office support'],\n",
              " 18273: ['it jobs'],\n",
              " 2529: ['healthcare & medical'],\n",
              " 21145: ['it jobs'],\n",
              " 10036: ['accounting'],\n",
              " 2627: ['administration & office support'],\n",
              " 989: ['telecommunication'],\n",
              " 20823: ['it jobs'],\n",
              " 1950: ['telecommunication'],\n",
              " 23210: ['it jobs'],\n",
              " 17723: ['it jobs'],\n",
              " 8684: ['sales'],\n",
              " 4305: ['sales'],\n",
              " 21020: ['it jobs'],\n",
              " 8050: ['administration & office support'],\n",
              " 21893: ['it jobs'],\n",
              " 6193: ['administration & office support'],\n",
              " 17024: ['it jobs'],\n",
              " 11401: ['accounting'],\n",
              " 15963: ['it jobs'],\n",
              " 19473: ['it jobs'],\n",
              " 23100: ['it jobs'],\n",
              " 11874: ['recruitment consultancy jobs'],\n",
              " 4406: ['administration & office support'],\n",
              " 5923: ['administration & office support'],\n",
              " 21760: ['it jobs'],\n",
              " 11262: ['accounting'],\n",
              " 16969: ['it jobs'],\n",
              " 18597: ['it jobs'],\n",
              " 2427: ['healthcare & medical'],\n",
              " 14879: ['it jobs'],\n",
              " 17046: ['it jobs'],\n",
              " 16073: ['it jobs'],\n",
              " 11052: ['marketing'],\n",
              " 17027: ['it jobs'],\n",
              " 23089: ['it jobs'],\n",
              " 6621: ['accounting'],\n",
              " 23009: ['it jobs'],\n",
              " 22464: ['it jobs'],\n",
              " 22175: ['it jobs'],\n",
              " 3861: ['telecommunication'],\n",
              " 11480: ['accounting'],\n",
              " 8077: ['sales'],\n",
              " 23060: ['it jobs'],\n",
              " 14224: ['sales'],\n",
              " 10839: ['retail & consumer products'],\n",
              " 21320: ['it jobs'],\n",
              " 2444: ['accounting'],\n",
              " 13865: ['accounting'],\n",
              " 16526: ['it jobs'],\n",
              " 3150: ['healthcare & medical'],\n",
              " 5418: ['telecommunication'],\n",
              " 15964: ['it jobs'],\n",
              " 10780: ['retail & consumer products'],\n",
              " 10997: ['marketing'],\n",
              " 19862: ['it jobs'],\n",
              " 4229: ['retail & consumer products'],\n",
              " 23376: ['it jobs'],\n",
              " 11393: ['accounting'],\n",
              " 120: ['healthcare & medical'],\n",
              " 11511: ['recruitment consultancy jobs'],\n",
              " 9544: ['healthcare & medical'],\n",
              " ...}"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(ground_truth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXsa_wbANjmb",
        "outputId": "9bd4fc1e-781b-439c-fe35-c28769f6714c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Precision@3 with 18979 trains and 4745 tests: 0.1252546540217773\n"
          ]
        }
      ],
      "source": [
        "def precision_at_k(recommendations, ground_truth, k=3):\n",
        "    \"\"\"\n",
        "    Calculate the precision at k for recommendation system.\n",
        "\n",
        "    Parameters:\n",
        "    - recommendations (dict): Dictionary where keys are user IDs and values are lists of recommended majors.\n",
        "    - ground_truth (dict): Dictionary where keys are user IDs and values are lists of truly suitable majors.\n",
        "    - k (int): The number of top recommendations to consider for calculating precision.\n",
        "\n",
        "    Returns:\n",
        "    - float: The average precision at k for all users.\n",
        "    \"\"\"\n",
        "    precision_scores = []\n",
        "\n",
        "    for applicant, recommended_major in recommendations.items():\n",
        "        if applicant in ground_truth:\n",
        "            # Get top k recommendations\n",
        "            top_k_recs = recommended_major[:k]\n",
        "            # Calculate the number of relevant recommendations\n",
        "            relevant_recs = sum(1 for major in top_k_recs if major in ground_truth[applicant])\n",
        "            # Precision at k for this user\n",
        "            precision = relevant_recs / k\n",
        "            precision_scores.append(precision)\n",
        "\n",
        "    # Average precision at k over all users\n",
        "    average_precision = np.mean(precision_scores) if precision_scores else 0\n",
        "    return average_precision\n",
        "\n",
        "avg_precision = precision_at_k(recommendations, ground_truth)\n",
        "print(\"Average Precision@3 with 18979 trains and 4745 tests:\", avg_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAIvtKEaRQml",
        "outputId": "7dd82dc6-0e1b-43d5-bc95-cb457cde5d72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average Recall@3 with 18979 trains and 4745 tests: 0.3757639620653319\n"
          ]
        }
      ],
      "source": [
        "def recall_at_k(recommendations, ground_truth, k=3):\n",
        "    recall_scores = []\n",
        "\n",
        "    for user_id, recommended_majors in recommendations.items():\n",
        "        if user_id in ground_truth:\n",
        "            # Get top k recommendations\n",
        "            top_k_recs = recommended_majors[:k]\n",
        "            # Calculate the number of relevant recommendations\n",
        "            relevant_recs = sum(1 for major in top_k_recs if major in ground_truth[user_id])\n",
        "            # Calculate the total number of relevant items\n",
        "            total_relevant = len(ground_truth[user_id])\n",
        "            # Recall at k for this user\n",
        "            recall = relevant_recs / total_relevant if total_relevant else 0\n",
        "            recall_scores.append(recall)\n",
        "\n",
        "    # Average recall at k over all users\n",
        "    average_recall = sum(recall_scores) / len(recall_scores) if recall_scores else 0\n",
        "    return average_recall\n",
        "\n",
        "# Example usage:\n",
        "avg_recall = recall_at_k(recommendations, ground_truth)\n",
        "print(\"Average Recall@3 with 18979 trains and 4745 tests:\", avg_recall)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUHBsQS_-5Eu",
        "outputId": "fdab3075-dab8-458e-e663-2564b20da97c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average F1 Score@3: 0.18788198103266596\n"
          ]
        }
      ],
      "source": [
        "def f1_score_at_k(recommendations, ground_truth, k=3):\n",
        "    precision = precision_at_k(recommendations, ground_truth, k)\n",
        "    recall = recall_at_k(recommendations, ground_truth, k)\n",
        "\n",
        "    if precision + recall == 0:\n",
        "        return 0\n",
        "\n",
        "    f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "    return f1_score\n",
        "\n",
        "avg_f1_score = f1_score_at_k(recommendations, ground_truth)\n",
        "\n",
        "print(\"Average F1 Score@3:\", avg_f1_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XH3s8joaERgV"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ycwN4z9QEMgl"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}