diff --git "a/Job_Recommendation_System.ipynb" "b/Job_Recommendation_System.ipynb" new file mode 100644--- /dev/null +++ "b/Job_Recommendation_System.ipynb" @@ -0,0 +1,6198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_NrjL2ccH3yp" + }, + "source": [ + "RECOMMENDATION MODEL" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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": 3, + "metadata": { + "id": "MV-7idG1F_NU" + }, + "outputs": [], + "source": [ + "# Mock data creation\n", + "def create_mock_data():\n", + " users_data = \"rematch_train_candidate_field.csv\"\n", + " applicants = pd.read_csv(users_data)\n", + "\n", + " jobs_data = \"jobs_data.csv\"\n", + " companies = pd.read_csv(jobs_data)\n", + "\n", + " train_applicants = applicants\n", + " test_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": 4, + "metadata": { + "id": "wF1oZ6Ez96BE" + }, + "outputs": [], + "source": [ + "train_user, test_user, jobs = create_mock_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Gj8tJNrph8Go", + "outputId": "a44b8cf0-a56f-4cd2-bbda-ca9bcabf35a0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data size: 23724\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": 6, + "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": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 213 + }, + "id": "JOZ9_NlLK8uS", + "outputId": "17d09f55-192f-4486-bb47-b56f525d44a3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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User IDcandidate_fieldlabelhard_skillsoft_skillfinal_hard_skillfinal_soft_skill
014649it jobs1['act', 'advertising sales', 'algorithms', 'bu...['collaboration', 'decision making', 'operatio...act, advertising sales, algorithms, business, ...collaboration, decision making, operations, wr...
1801marketing0['act', 'brand communication', 'business', 'bu...['collaboration', 'customer service', 'managem...act, brand communication, business, business d...collaboration, customer service, management
24393accounting0['application', 'balance sheet', 'finance', 'p...['filing', 'management']application, balance sheet, finance, property ...filing, management
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" + ], + "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": 7, + "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)\n", + "test_user.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 213 + }, + "id": "GC8bn3cjB8D5", + "outputId": "436e843d-425e-4ce2-e551-e4f249bdd10b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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User IDcandidate_fieldlabelhard_skillsoft_skillfinal_hard_skillfinal_soft_skill
01retail & consumer products0['business', 'merchandising', 'sales', 'service']['customer service']business, merchandising, sales, servicecustomer service
12sales0['application', 'business', 'business requirem...['accountability', 'collaboration', 'innovatio...application, business, business requirements, ...accountability, collaboration, innovation, man...
23healthcare & medical0['application', 'cancer', 'endocrinology', 'hy...['research', 'training and development']application, cancer, endocrinology, hydrothera...research, training and development
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" + ], + "text/plain": [ + " User ID candidate_field label \\\n", + "0 1 retail & consumer products 0 \n", + "1 2 sales 0 \n", + "2 3 healthcare & medical 0 \n", + "\n", + " hard_skill \\\n", + "0 ['business', 'merchandising', 'sales', 'service'] \n", + "1 ['application', 'business', 'business requirem... \n", + "2 ['application', 'cancer', 'endocrinology', 'hy... \n", + "\n", + " soft_skill \\\n", + "0 ['customer service'] \n", + "1 ['accountability', 'collaboration', 'innovatio... \n", + "2 ['research', 'training and development'] \n", + "\n", + " final_hard_skill \\\n", + "0 business, merchandising, sales, service \n", + "1 application, business, business requirements, ... \n", + "2 application, cancer, endocrinology, hydrothera... \n", + "\n", + " final_soft_skill \n", + "0 customer service \n", + "1 accountability, collaboration, innovation, man... \n", + "2 research, training and development " + ] + }, + "execution_count": 9, + "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": 10, + "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": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 213 + }, + "id": "knFii8o3EQmv", + "outputId": "47afb484-0765-4ad9-8765-d084673450ac" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Job IDMajorHard SkillsSoft Skillsfinal_hard_skillfinal_soft_skill
01accounting['business', 'finance', 'excel', 'tax', 'servi...['management', 'planning', 'operations', 'lead...business, finance, excel, tax, service, data, ...management, planning, operations, leadership, ...
12administration & office support['service', 'business', 'data', 'excel', 'appl...['management', 'customer service', 'microsoft ...service, business, data, excel, application, s...management, customer service, microsoft office...
23advertising, arts & media['business', 'digital', 'sales', 'service', 'a...['management', 'social media', 'writing', 'com...business, digital, sales, service, application...management, social media, writing, communicati...
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" + ], + "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": 11, + "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": 12, + "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, tfidf_vectorizer_skills, tfidf_vectorizer_majors)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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": 14, + "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": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "Ajxp0xelIrl2", + "outputId": "08bafc5b-73cc-4695-924a-931840047dd5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recommendations for each applicant:\n", + "14649: ['retail & consumer products' 'healthcare & medical' 'it jobs']\n", + "801: ['sales' 'administration & office support' 'marketing']\n", + "4393: ['healthcare & medical' 'advertising, arts & media' 'sales']\n", + "10045: ['advertising, arts & media' 'administration & office support' 'it jobs']\n", + "11347: ['marketing' 'sales' 'administration & office support']\n", + "15999: ['administration & office support' 'healthcare & medical' 'it jobs']\n", + "15760: ['marketing' 'it jobs' 'accounting']\n", + "18080: ['administration & office support' 'accounting' 'it jobs']\n", + "20822: ['healthcare & medical' 'it jobs' 'accounting']\n", + "19869: ['marketing' 'it jobs' 'advertising, arts & media']\n", + "11027: ['healthcare & medical' 'it jobs' 'accounting']\n", + "15846: ['retail & consumer products' 'administration & office support' 'it jobs']\n", + "10849: ['administration & office support' 'it jobs' 'marketing']\n", + "3153: ['accounting' 'healthcare & medical' 'it jobs']\n", + "11942: ['accounting' 'advertising, arts & media' 'it jobs']\n", + "2571: ['healthcare & medical' 'it jobs' 'accounting']\n", + "4355: ['sales' 'marketing' 'it jobs']\n", + "10479: ['it jobs' 'healthcare & medical' 'advertising, arts & media']\n", + "2755: ['administration & office support' 'it jobs' 'accounting']\n", + "3970: ['sport & recreation' 'accounting' 'administration & office support']\n", + "16927: ['sales' 'administration & office support' 'marketing']\n", + "4068: ['healthcare & medical' 'it jobs' 'administration & office support']\n", + "15017: ['healthcare & medical' 'accounting' 'it jobs']\n", + "18881: ['sales' 'it jobs' 'accounting']\n", + "12193: ['administration & office support' 'advertising, arts & media' 'marketing']\n", + "13513: ['healthcare & medical' 'sport & recreation' 'it jobs']\n", + "153: ['accounting' 'administration & office support' 'it jobs']\n", + "1737: ['administration & office support' 'marketing' 'healthcare & medical']\n", + "18685: ['accounting' 'advertising, arts & media'\n", + " 'administration & office support']\n", + "10942: ['marketing' 'administration & office support' 'accounting']\n", + "21187: ['sales' 'advertising, arts & media' 'it jobs']\n", + "21139: ['healthcare & medical' 'marketing' 'advertising, arts & media']\n", + "20363: ['administration & office support' 'it jobs' 'accounting']\n", + "249: ['it jobs' 'accounting' 'administration & office support']\n", + "11218: ['marketing' 'accounting' 'administration & office support']\n", + "16430: ['sales' 'healthcare & medical' 'telecomunication']\n", + "14184: ['healthcare & medical' 'administration & office support'\n", + " 'advertising, arts & media']\n", + "9032: ['healthcare & medical' 'accounting' 'administration & office support']\n", + "4497: ['administration & office support' 'marketing' 'advertising, arts & media']\n", + "17928: ['administration & office support' 'it jobs' 'accounting']\n", + "17468: ['accounting' 'administration & office support' 'it jobs']\n", + "1148: ['healthcare & medical' 'administration & office support' 'it jobs']\n", + "3461: ['retail & consumer products' 'accounting' 'marketing']\n", + "14272: ['administration & office support' 'accounting' 'it jobs']\n", + "20122: ['accounting' 'marketing' 'advertising, arts & media']\n", + "7108: ['accounting' 'marketing' 'advertising, arts & media']\n", + "14515: ['administration & office support' 'healthcare & medical' 'accounting']\n", + "11319: ['administration & office support' 'it jobs' 'advertising, arts & media']\n", + "533: ['marketing' 'advertising, arts & media' 'it jobs']\n", + "498: ['accounting' 'advertising, arts & media' 'marketing']\n", + "23240: ['retail & consumer products' 'advertising, arts & media' 'sales']\n", + "5792: ['administration & office support' 'healthcare & medical' 'it jobs']\n", + "7787: ['advertising, arts & media' 'administration & office support' 'it jobs']\n", + "7811: ['administration & office support' 'marketing' 'advertising, arts & media']\n", + "12892: ['administration & office support' 'it jobs' 'marketing']\n", + "7496: ['accounting' 'administration & office support' 'healthcare & medical']\n", + "18157: ['administration & office support' 'it jobs' 'accounting']\n", + "12073: ['administration & office support' 'it jobs' 'accounting']\n", + "18434: ['healthcare & medical' 'administration & office support' 'accounting']\n", + "18993: ['healthcare & medical' 'it jobs' 'accounting']\n", + "15169: ['healthcare & medical' 'advertising, arts & media'\n", + " 'administration & office support']\n", + "18859: ['marketing' 'it jobs' 'administration & office support']\n", + "106: ['administration & office support' 'telecomunication' 'sport & recreation']\n", + "15455: ['accounting' 'it jobs' 'marketing']\n", + "9760: ['administration & office support' 'marketing' 'advertising, arts & media']\n", + "5411: ['healthcare & medical' 'marketing' 'it jobs']\n", + "5413: ['healthcare & medical' 'accounting' 'marketing']\n", + "19366: ['accounting' 'administration & office support' 'it jobs']\n", + "875: ['healthcare & medical' 'administration & office support' 'it jobs']\n", + "14128: ['accounting' 'marketing' 'healthcare & medical']\n", + "10638: ['retail & consumer products' 'administration & office support' 'it 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'administration & office support' 'it jobs']\n", + "7495: ['accounting' 'healthcare & medical' 'it jobs']\n", + "7406: ['accounting' 'administration & office support' 'healthcare & medical']\n", + "19374: ['healthcare & medical' 'it jobs' 'administration & office support']\n", + "11059: ['sales' 'advertising, arts & media' 'marketing']\n", + "14654: ['administration & office support' 'it jobs' 'marketing']\n", + "19782: ['sport & recreation' 'healthcare & medical' 'it jobs']\n", + "19200: ['accounting' 'administration & office support' 'it jobs']\n", + "23030: ['advertising, arts & media' 'marketing' 'administration & office support']\n", + "23209: ['healthcare & medical' 'accounting' 'it jobs']\n", + "21754: ['marketing' 'it jobs' 'healthcare & medical']\n", + "10224: ['healthcare & medical' 'marketing' 'accounting']\n", + "3221: ['administration & office support' 'advertising, arts & media' 'it jobs']\n", + "17907: ['administration & office support' 'accounting' 'it jobs']\n", + "4706: ['sales' 'administration & office support' 'it jobs']\n", + "20636: ['sales' 'healthcare & medical' 'it jobs']\n", + "15070: ['healthcare & medical' 'marketing' 'accounting']\n", + "23576: ['sales' 'accounting' 'it jobs']\n", + "10769: ['administration & office support' 'healthcare & medical' 'marketing']\n", + "18420: ['retail & consumer products' 'administration & office support'\n", + " 'advertising, arts & media']\n", + "19650: ['accounting' 'healthcare & medical' 'marketing']\n", + "19454: ['advertising, arts & media' 'marketing' 'it jobs']\n", + "4655: ['advertising, arts & media' 'marketing' 'administration & office support']\n", + "18944: ['administration & office support' 'advertising, arts & media'\n", + " 'accounting']\n", + "1423: ['administration & office support' 'advertising, arts & media'\n", + " 'accounting']\n", + "13025: ['it jobs' 'administration & office support' 'accounting']\n", + "3075: ['healthcare & medical' 'it jobs' 'accounting']\n", + "19190: ['marketing' 'advertising, arts & media' 'it jobs']\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, tfidf_vectorizer_skills, tfidf_vectorizer_majors = feature_engineering(train_user, jobs)\n", + "\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": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nj-HEdyJlYNY", + "outputId": "063b84bc-5717-4a0c-8367-939a054657bc" + }, + "outputs": [], + "source": [ + "# Process input skills and recommend jobs\n", + "def 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", + " 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 = jobs.iloc[sorted_company_indices]['Major'].values[:3]\n", + "\n", + " return recommended_companies" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IMTilMnQINZC" + }, + "source": [ + "TEST RECOMMENDED SYSTEM" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recommended Jobs based on input skills and major:\n", + "['it jobs' 'sales' 'administration & office support']\n" + ] + } + ], + "source": [ + "input_hard_skills = \"Java, Excel, Python\"\n", + "input_soft_skills = \"Communication, Teamwork\"\n", + "input_major = \"Economy\"\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": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "import joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the components\n", + "joblib.dump(tfidf_vectorizer_skills, 'tfidf_vectorizer_skills.pkl')\n", + "joblib.dump(tfidf_vectorizer_majors, 'tfidf_vectorizer_majors.pkl')\n", + "joblib.dump(companies_skills_vec, 'companies_skills_vec.pkl')\n", + "joblib.dump(companies_majors_vec, 'companies_majors_vec.pkl')\n", + "\n", + "# Load the components\n", + "tfidf_vectorizer_skills = joblib.load('tfidf_vectorizer_skills.pkl')\n", + "tfidf_vectorizer_majors = joblib.load('tfidf_vectorizer_majors.pkl')\n", + "companies_skills_vec = joblib.load('companies_skills_vec.pkl')\n", + "companies_majors_vec = joblib.load('companies_majors_vec.pkl')" + ] + }, + { + "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": [], + "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": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c5a2a73185a045f6bef2b86f248e73d9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + 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