{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ๐Ÿง  HRHUB v2.1 - Enhanced with LLM (FREE VERSION)\n", "\n", "## ๐Ÿ“˜ Project Overview\n", "\n", "**Bilateral HR Matching System with LLM-Powered Intelligence**\n", "\n", "### What's New in v2.1:\n", "- โœ… **FREE LLM**: Using Hugging Face Inference API (no cost)\n", "- โœ… **Job Level Classification**: Zero-shot & few-shot learning\n", "- โœ… **Structured Skills Extraction**: Pydantic schemas\n", "- โœ… **Match Explainability**: LLM-generated reasoning\n", "- โœ… **Flexible Data Loading**: Upload OR Google Drive\n", "\n", "### Tech Stack:\n", "```\n", "Embeddings: sentence-transformers (local, free)\n", "LLM: Hugging Face Inference API (free tier)\n", "Schemas: Pydantic\n", "Platform: Google Colab โ†’ VS Code\n", "```\n", "\n", "---\n", "\n", "**Master's Thesis - Aalborg University** \n", "*Business Data Science Program* \n", "*December 2025*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 1: Install Dependencies" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… All packages installed!\n" ] } ], "source": [ "# Install required packages\n", "#!pip install -q sentence-transformers huggingface-hub pydantic plotly pyvis nbformat scikit-learn pandas numpy\n", "\n", "print(\"โœ… All packages installed!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 2: Import Libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Environment variables loaded from .env\n", "โœ… All libraries imported!\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import json\n", "import os\n", "from typing import List, Dict, Optional, Literal\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# ML & NLP\n", "from sentence_transformers import SentenceTransformer\n", "from sklearn.metrics.pairwise import cosine_similarity\n", "\n", "# LLM Integration (FREE)\n", "from huggingface_hub import InferenceClient\n", "from pydantic import BaseModel, Field\n", "\n", "# Visualization\n", "import plotly.graph_objects as go\n", "from IPython.display import HTML, display\n", "\n", "# Configuration Settings\n", "from dotenv import load_dotenv\n", "\n", "# Carrega variรกveis do .env\n", "load_dotenv()\n", "print(\"โœ… Environment variables loaded from .env\")\n", "\n", "print(\"โœ… All libraries imported!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 3: Configuration" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Configuration loaded!\n", "๐Ÿง  Embedding model: all-MiniLM-L6-v2\n", "๐Ÿค– LLM model: meta-llama/Llama-3.2-3B-Instruct\n", "๐Ÿ”‘ HF Token configured: Yes โœ…\n", "๐Ÿ“‚ Data path: ../csv_files/\n" ] } ], "source": [ "class Config:\n", " \"\"\"Centralized configuration for VS Code\"\"\"\n", " \n", " # Paths - VS Code structure\n", " CSV_PATH = '../csv_files/'\n", " PROCESSED_PATH = '../processed/'\n", " RESULTS_PATH = '../results/'\n", " \n", " # Embedding Model\n", " EMBEDDING_MODEL = 'all-MiniLM-L6-v2'\n", " \n", " # LLM Settings (FREE - Hugging Face)\n", " HF_TOKEN = os.getenv('HF_TOKEN', '') # โœ… Pega do .env\n", " LLM_MODEL = 'meta-llama/Llama-3.2-3B-Instruct'\n", " \n", " LLM_MAX_TOKENS = 1000\n", " \n", " # Matching Parameters\n", " TOP_K_MATCHES = 10\n", " SIMILARITY_THRESHOLD = 0.5\n", " RANDOM_SEED = 42\n", "\n", "np.random.seed(Config.RANDOM_SEED)\n", "\n", "print(\"โœ… Configuration loaded!\")\n", "print(f\"๐Ÿง  Embedding model: {Config.EMBEDDING_MODEL}\")\n", "print(f\"๐Ÿค– LLM model: {Config.LLM_MODEL}\")\n", "print(f\"๐Ÿ”‘ HF Token configured: {'Yes โœ…' if Config.HF_TOKEN else 'No โš ๏ธ'}\")\n", "print(f\"๐Ÿ“‚ Data path: {Config.CSV_PATH}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ—๏ธ Step 4: Architecture - Text Builders\n", "\n", "**HIGH COHESION:** Each class has ONE responsibility\n", "**LOW COUPLING:** Classes don't depend on each other" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Text Builder classes loaded\n", " โ€ข CandidateTextBuilder\n", " โ€ข CompanyTextBuilder\n" ] } ], "source": [ "# ============================================================================\n", "# TEXT BUILDER CLASSES - Single Responsibility Principle\n", "# ============================================================================\n", "\n", "from abc import ABC, abstractmethod\n", "from typing import List\n", "\n", "class TextBuilder(ABC):\n", " \"\"\"Abstract base class for text builders\"\"\"\n", " \n", " @abstractmethod\n", " def build(self, row: pd.Series) -> str:\n", " \"\"\"Build text representation from DataFrame row\"\"\"\n", " pass\n", " \n", " def build_batch(self, df: pd.DataFrame) -> List[str]:\n", " \"\"\"Build text representations for entire DataFrame\"\"\"\n", " return df.apply(self.build, axis=1).tolist()\n", "\n", "\n", "class CandidateTextBuilder(TextBuilder):\n", " \"\"\"Builds text representation for candidates\"\"\"\n", " \n", " def __init__(self, fields: List[str] = None):\n", " self.fields = fields or [\n", " 'Category',\n", " 'skills',\n", " 'career_objective',\n", " 'degree_names',\n", " 'positions'\n", " ]\n", " \n", " def build(self, row: pd.Series) -> str:\n", " parts = []\n", " \n", " if row.get('Category'):\n", " parts.append(f\"Job Category: {row['Category']}\")\n", " \n", " if row.get('skills'):\n", " parts.append(f\"Skills: {row['skills']}\")\n", " \n", " if row.get('career_objective'):\n", " parts.append(f\"Objective: {row['career_objective']}\")\n", " \n", " if row.get('degree_names'):\n", " parts.append(f\"Education: {row['degree_names']}\")\n", " \n", " if row.get('positions'):\n", " parts.append(f\"Experience: {row['positions']}\")\n", " \n", " return ' '.join(parts)\n", "\n", "\n", "class CompanyTextBuilder(TextBuilder):\n", " \"\"\"Builds text representation for companies\"\"\"\n", " \n", " def __init__(self, include_postings: bool = True):\n", " self.include_postings = include_postings\n", " \n", " def build(self, row: pd.Series) -> str:\n", " parts = []\n", " \n", " if row.get('name'):\n", " parts.append(f\"Company: {row['name']}\")\n", " \n", " if row.get('description'):\n", " parts.append(f\"Description: {row['description']}\")\n", " \n", " if row.get('industries_list'):\n", " parts.append(f\"Industries: {row['industries_list']}\")\n", " \n", " if row.get('specialties_list'):\n", " parts.append(f\"Specialties: {row['specialties_list']}\")\n", " \n", " # Include job postings data (THE BRIDGE!)\n", " if self.include_postings:\n", " if row.get('required_skills'):\n", " parts.append(f\"Required Skills: {row['required_skills']}\")\n", " \n", " if row.get('posted_job_titles'):\n", " parts.append(f\"Job Titles: {row['posted_job_titles']}\")\n", " \n", " if row.get('experience_levels'):\n", " parts.append(f\"Experience: {row['experience_levels']}\")\n", " \n", " return ' '.join(parts)\n", "\n", "\n", "print(\"โœ… Text Builder classes loaded\")\n", "print(\" โ€ข CandidateTextBuilder\")\n", "print(\" โ€ข CompanyTextBuilder\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ—๏ธ Step 5: Architecture - Embedding Manager\n", "\n", "**Responsibility:** Generate, save, and load embeddings" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… EmbeddingManager class loaded\n" ] } ], "source": [ "# ============================================================================\n", "# EMBEDDING MANAGER - Handles all embedding operations\n", "# ============================================================================\n", "\n", "from pathlib import Path\n", "from typing import Tuple, Optional\n", "\n", "class EmbeddingManager:\n", " \"\"\"Manages embedding generation, saving, and loading\"\"\"\n", " \n", " def __init__(self, model: SentenceTransformer, save_dir: str):\n", " self.model = model\n", " self.save_dir = Path(save_dir)\n", " self.save_dir.mkdir(parents=True, exist_ok=True)\n", " \n", " def _get_file_paths(self, entity_type: str) -> Tuple[Path, Path]:\n", " \"\"\"Get file paths for embeddings and metadata\"\"\"\n", " emb_file = self.save_dir / f\"{entity_type}_embeddings.npy\"\n", " meta_file = self.save_dir / f\"{entity_type}_metadata.pkl\"\n", " return emb_file, meta_file\n", " \n", " def exists(self, entity_type: str) -> bool:\n", " \"\"\"Check if embeddings exist for entity type\"\"\"\n", " emb_file, _ = self._get_file_paths(entity_type)\n", " return emb_file.exists()\n", " \n", " def load(self, entity_type: str) -> Tuple[np.ndarray, pd.DataFrame]:\n", " \"\"\"Load embeddings and metadata\"\"\"\n", " emb_file, meta_file = self._get_file_paths(entity_type)\n", " \n", " if not emb_file.exists():\n", " raise FileNotFoundError(f\"Embeddings not found: {emb_file}\")\n", " \n", " embeddings = np.load(emb_file)\n", " metadata = pd.read_pickle(meta_file) if meta_file.exists() else None\n", " \n", " return embeddings, metadata\n", " \n", " def generate(self,\n", " texts: List[str],\n", " batch_size: int = 32,\n", " show_progress: bool = True) -> np.ndarray:\n", " \"\"\"Generate embeddings from texts\"\"\"\n", " return self.model.encode(\n", " texts,\n", " batch_size=batch_size,\n", " show_progress_bar=show_progress,\n", " normalize_embeddings=True,\n", " convert_to_numpy=True\n", " )\n", " \n", " def save(self,\n", " entity_type: str,\n", " embeddings: np.ndarray,\n", " metadata: pd.DataFrame) -> None:\n", " \"\"\"Save embeddings and metadata\"\"\"\n", " emb_file, meta_file = self._get_file_paths(entity_type)\n", " \n", " np.save(emb_file, embeddings)\n", " metadata.to_pickle(meta_file)\n", " \n", " print(f\"๐Ÿ’พ Saved:\")\n", " print(f\" {emb_file}\")\n", " print(f\" {meta_file}\")\n", " \n", " def generate_and_save(self,\n", " entity_type: str,\n", " texts: List[str],\n", " metadata: pd.DataFrame,\n", " batch_size: int = 32) -> np.ndarray:\n", " \"\"\"Generate embeddings and save everything\"\"\"\n", " print(f\"๐Ÿ”„ Generating {entity_type} embeddings...\")\n", " print(f\" Processing {len(texts):,} items...\")\n", " \n", " embeddings = self.generate(texts, batch_size=batch_size)\n", " self.save(entity_type, embeddings, metadata)\n", " \n", " return embeddings\n", " \n", " def load_or_generate(self,\n", " entity_type: str,\n", " texts: List[str],\n", " metadata: pd.DataFrame,\n", " force_regenerate: bool = False) -> Tuple[np.ndarray, pd.DataFrame]:\n", " \"\"\"Load if exists, generate otherwise\"\"\"\n", " \n", " if not force_regenerate and self.exists(entity_type):\n", " print(f\"๐Ÿ“ฅ Loading {entity_type} embeddings...\")\n", " embeddings, saved_metadata = self.load(entity_type)\n", " \n", " # Verify alignment\n", " if len(embeddings) != len(metadata):\n", " print(f\"โš ๏ธ Size mismatch! Regenerating...\")\n", " embeddings = self.generate_and_save(\n", " entity_type, texts, metadata\n", " )\n", " else:\n", " print(f\"โœ… Loaded: {embeddings.shape}\")\n", " else:\n", " embeddings = self.generate_and_save(\n", " entity_type, texts, metadata\n", " )\n", " \n", " return embeddings, metadata\n", "\n", "\n", "print(\"โœ… EmbeddingManager class loaded\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ—๏ธ Step 6: Architecture - Matching Engine\n", "\n", "**Responsibility:** Calculate similarities and find matches" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… MatchingEngine class loaded\n" ] } ], "source": [ "# ============================================================================\n", "# MATCHING ENGINE - Handles similarity calculations\n", "# ============================================================================\n", "\n", "class MatchingEngine:\n", " \"\"\"Calculates similarities and finds top matches\"\"\"\n", " \n", " def __init__(self,\n", " candidate_vectors: np.ndarray,\n", " company_vectors: np.ndarray,\n", " candidate_metadata: pd.DataFrame,\n", " company_metadata: pd.DataFrame):\n", " \n", " self.cand_vectors = candidate_vectors\n", " self.comp_vectors = company_vectors\n", " self.cand_metadata = candidate_metadata\n", " self.comp_metadata = company_metadata\n", " \n", " # Verify alignment\n", " assert len(candidate_vectors) == len(candidate_metadata), \\\n", " \"Candidate embeddings and metadata size mismatch\"\n", " assert len(company_vectors) == len(company_metadata), \\\n", " \"Company embeddings and metadata size mismatch\"\n", " \n", " def find_matches(self,\n", " candidate_idx: int,\n", " top_k: int = 10) -> List[Tuple[int, float]]:\n", " \"\"\"Find top K company matches for a candidate\"\"\"\n", " \n", " if candidate_idx >= len(self.cand_vectors):\n", " raise IndexError(f\"Candidate index {candidate_idx} out of range\")\n", " \n", " # Get candidate vector\n", " cand_vec = self.cand_vectors[candidate_idx].reshape(1, -1)\n", " \n", " # Calculate similarities\n", " similarities = cosine_similarity(cand_vec, self.comp_vectors)[0]\n", " \n", " # Get top K\n", " top_indices = np.argsort(similarities)[::-1][:top_k]\n", " \n", " # Return (index, score) tuples\n", " return [(int(idx), float(similarities[idx])) for idx in top_indices]\n", " \n", " def get_match_details(self,\n", " candidate_idx: int,\n", " company_idx: int) -> dict:\n", " \"\"\"Get detailed match information\"\"\"\n", " \n", " candidate = self.cand_metadata.iloc[candidate_idx]\n", " company = self.comp_metadata.iloc[company_idx]\n", " \n", " # Calculate similarity\n", " cand_vec = self.cand_vectors[candidate_idx].reshape(1, -1)\n", " comp_vec = self.comp_vectors[company_idx].reshape(1, -1)\n", " similarity = float(cosine_similarity(cand_vec, comp_vec)[0][0])\n", " \n", " return {\n", " 'candidate': candidate.to_dict(),\n", " 'company': company.to_dict(),\n", " 'similarity_score': similarity\n", " }\n", " \n", " def batch_match(self,\n", " candidate_indices: List[int],\n", " top_k: int = 10) -> dict:\n", " \"\"\"Find matches for multiple candidates\"\"\"\n", " \n", " results = {}\n", " for idx in candidate_indices:\n", " results[idx] = self.find_matches(idx, top_k=top_k)\n", " \n", " return results\n", "\n", "\n", "print(\"โœ… MatchingEngine class loaded\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 7: Load All Datasets" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ“‚ Loading all datasets...\n", "\n", "======================================================================\n", "โœ… Candidates: 9,544 rows ร— 35 columns\n", "โœ… Companies (base): 24,473 rows\n", "โœ… Company industries: 24,375 rows\n", "โœ… Company specialties: 169,387 rows\n", "โœ… Employee counts: 35,787 rows\n", "โœ… Postings: 123,849 rows ร— 31 columns\n", "โœ… Job skills: 213,768 rows\n", "โœ… Job industries: 164,808 rows\n", "\n", "======================================================================\n", "โœ… All datasets loaded successfully!\n", "\n" ] } ], "source": [ "print(\"๐Ÿ“‚ Loading all datasets...\\n\")\n", "print(\"=\" * 70)\n", "\n", "# Load main datasets\n", "candidates = pd.read_csv(f'{Config.CSV_PATH}resume_data.csv')\n", "print(f\"โœ… Candidates: {len(candidates):,} rows ร— {len(candidates.columns)} columns\")\n", "\n", "companies_base = pd.read_csv(f'{Config.CSV_PATH}companies.csv')\n", "print(f\"โœ… Companies (base): {len(companies_base):,} rows\")\n", "\n", "company_industries = pd.read_csv(f'{Config.CSV_PATH}company_industries.csv')\n", "print(f\"โœ… Company industries: {len(company_industries):,} rows\")\n", "\n", "company_specialties = pd.read_csv(f'{Config.CSV_PATH}company_specialities.csv')\n", "print(f\"โœ… Company specialties: {len(company_specialties):,} rows\")\n", "\n", "employee_counts = pd.read_csv(f'{Config.CSV_PATH}employee_counts.csv')\n", "print(f\"โœ… Employee counts: {len(employee_counts):,} rows\")\n", "\n", "postings = pd.read_csv(f'{Config.CSV_PATH}postings.csv', on_bad_lines='skip', engine='python')\n", "print(f\"โœ… Postings: {len(postings):,} rows ร— {len(postings.columns)} columns\")\n", "\n", "# Optional datasets\n", "try:\n", " job_skills = pd.read_csv(f'{Config.CSV_PATH}job_skills.csv')\n", " print(f\"โœ… Job skills: {len(job_skills):,} rows\")\n", "except:\n", " job_skills = None\n", " print(\"โš ๏ธ Job skills not found (optional)\")\n", "\n", "try:\n", " job_industries = pd.read_csv(f'{Config.CSV_PATH}job_industries.csv')\n", " print(f\"โœ… Job industries: {len(job_industries):,} rows\")\n", "except:\n", " job_industries = None\n", " print(\"โš ๏ธ Job industries not found (optional)\")\n", "\n", "print(\"\\n\" + \"=\" * 70)\n", "print(\"โœ… All datasets loaded successfully!\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 8: Merge & Enrich Company Data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ”„ ENRICHING COMPANY DATA...\n", "================================================================================\n", "\n", "1๏ธโƒฃ Aggregating industries...\n", "โœ… Industries aggregated: 24,365 companies\n", "\n", "2๏ธโƒฃ Aggregating specialties...\n", "โœ… Specialties aggregated: 17,780 companies\n", "\n", "3๏ธโƒฃ Aggregating job posting skills...\n", "โœ… Skills aggregated: 126,807 job postings\n", "\n", "4๏ธโƒฃ Aggregating job postings...\n", "โœ… Job data aggregated: 24,474 companies\n", "\n", "5๏ธโƒฃ Merging all data...\n", "โœ… Shape: (24473, 17)\n", "\n", "6๏ธโƒฃ Filling nulls...\n", " โœ… name 1 โ†’ 0\n", " โœ… description 297 โ†’ 0\n", " โœ… industries_list 108 โ†’ 0\n", " โœ… specialties_list 6,693 โ†’ 0\n", " โœ… avg_med_salary 22,312 โ†’ 0\n", " โœ… avg_max_salary 15,261 โ†’ 0\n", "\n", "7๏ธโƒฃ Validation...\n", "================================================================================\n", "โœ… name 0 issues\n", "โœ… description 0 issues\n", "โœ… industries_list 0 issues\n", "โœ… specialties_list 0 issues\n", "โŒ required_skills 945 issues\n", "โœ… posted_job_titles 0 issues\n", "================================================================================\n", "โš ๏ธ ISSUES!\n", "\n", "Total: 24,473\n", "With postings: 24,473\n" ] } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 8: Merge & Enrich Company Data + Empty Columns Validation\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "print(\"๐Ÿ”„ ENRICHING COMPANY DATA...\")\n", "print(\"=\" * 80)\n", "\n", "# ============================================================================\n", "# STEP 1: Aggregate Industries per Company\n", "# ============================================================================\n", "print(\"\\n1๏ธโƒฃ Aggregating industries...\")\n", "\n", "industries_grouped = company_industries.groupby('company_id')['industry'].apply(\n", " lambda x: ', '.join(x.dropna().astype(str).unique())\n", ").reset_index()\n", "industries_grouped.columns = ['company_id', 'industries_list']\n", "\n", "print(f\"โœ… Industries aggregated: {len(industries_grouped):,} companies\")\n", "\n", "# ============================================================================\n", "# STEP 2: Aggregate Specialties per Company\n", "# ============================================================================\n", "print(\"\\n2๏ธโƒฃ Aggregating specialties...\")\n", "\n", "specialties_grouped = company_specialties.groupby('company_id')['speciality'].apply(\n", " lambda x: ', '.join(x.dropna().astype(str).unique())\n", ").reset_index()\n", "specialties_grouped.columns = ['company_id', 'specialties_list']\n", "\n", "print(f\"โœ… Specialties aggregated: {len(specialties_grouped):,} companies\")\n", "\n", "# ============================================================================\n", "# STEP 3: Aggregate Skills from Job Postings\n", "# ============================================================================\n", "print(\"\\n3๏ธโƒฃ Aggregating job posting skills...\")\n", "\n", "if job_skills is not None:\n", " skills_df = pd.read_csv(f'{Config.CSV_PATH}skills.csv')\n", " \n", " job_skills_enriched = job_skills.merge(\n", " skills_df,\n", " on='skill_abr',\n", " how='left'\n", " )\n", " \n", " skills_per_posting = job_skills_enriched.groupby('job_id')['skill_name'].apply(\n", " lambda x: ', '.join(x.dropna().astype(str).unique())\n", " ).reset_index()\n", " skills_per_posting.columns = ['job_id', 'required_skills']\n", " \n", " print(f\"โœ… Skills aggregated: {len(skills_per_posting):,} job postings\")\n", "else:\n", " skills_per_posting = pd.DataFrame(columns=['job_id', 'required_skills'])\n", " print(\"โš ๏ธ Job skills not available\")\n", "\n", "# ============================================================================\n", "# STEP 4: Aggregate Job Posting Data per Company\n", "# ============================================================================\n", "print(\"\\n4๏ธโƒฃ Aggregating job postings...\")\n", "\n", "postings_enriched = postings.merge(skills_per_posting, on='job_id', how='left')\n", "\n", "job_data_grouped = postings_enriched.groupby('company_id').agg({\n", " 'title': lambda x: ', '.join(x.dropna().astype(str).unique()[:10]),\n", " 'required_skills': lambda x: ', '.join(x.dropna().astype(str).unique()),\n", " 'med_salary': 'mean',\n", " 'max_salary': 'mean',\n", " 'job_id': 'count'\n", "}).reset_index()\n", "\n", "job_data_grouped.columns = [\n", " 'company_id', 'posted_job_titles', 'required_skills', \n", " 'avg_med_salary', 'avg_max_salary', 'total_postings'\n", "]\n", "\n", "print(f\"โœ… Job data aggregated: {len(job_data_grouped):,} companies\")\n", "\n", "# ============================================================================\n", "# STEP 5: Merge Everything\n", "# ============================================================================\n", "print(\"\\n5๏ธโƒฃ Merging all data...\")\n", "\n", "companies_full = companies_base.copy()\n", "companies_full = companies_full.merge(industries_grouped, on='company_id', how='left')\n", "companies_full = companies_full.merge(specialties_grouped, on='company_id', how='left')\n", "companies_full = companies_full.merge(job_data_grouped, on='company_id', how='left')\n", "\n", "print(f\"โœ… Shape: {companies_full.shape}\")\n", "\n", "# ============================================================================\n", "# STEP 6: Fill Empty Columns\n", "# ============================================================================\n", "print(\"\\n6๏ธโƒฃ Filling nulls...\")\n", "\n", "fill_values = {\n", " 'name': 'Unknown Company',\n", " 'description': 'No description',\n", " 'industries_list': 'General',\n", " 'specialties_list': 'Not specified',\n", " 'required_skills': 'Not specified',\n", " 'posted_job_titles': 'Various',\n", " 'avg_med_salary': 0,\n", " 'avg_max_salary': 0,\n", " 'total_postings': 0\n", "}\n", "\n", "for col, val in fill_values.items():\n", " if col in companies_full.columns:\n", " before = companies_full[col].isna().sum()\n", " companies_full[col] = companies_full[col].fillna(val)\n", " if before > 0:\n", " print(f\" โœ… {col:25s} {before:>6,} โ†’ 0\")\n", "\n", "# ============================================================================\n", "# STEP 7: Validation\n", "# ============================================================================\n", "print(\"\\n7๏ธโƒฃ Validation...\")\n", "print(\"=\" * 80)\n", "\n", "critical = ['name', 'description', 'industries_list', 'specialties_list', \n", " 'required_skills', 'posted_job_titles']\n", "\n", "ok = True\n", "for col in critical:\n", " if col in companies_full.columns:\n", " issues = companies_full[col].isna().sum() + (companies_full[col] == '').sum()\n", " print(f\"{'โœ…' if issues == 0 else 'โŒ'} {col:25s} {issues} issues\")\n", " if issues > 0:\n", " ok = False\n", "\n", "print(\"=\" * 80)\n", "print(f\"{'๐ŸŽฏ PERFECT!' if ok else 'โš ๏ธ ISSUES!'}\")\n", "print(f\"\\nTotal: {len(companies_full):,}\")\n", "print(f\"With postings: {(companies_full['total_postings'] > 0).sum():,}\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” FILLING MISSING REQUIRED SKILLS...\n", "================================================================================\n", "โœ… Loaded 35 unique skills\n", "๐Ÿ” Found 0 companies with missing skills\n", "โœ… No missing skills to fill!\n", "\n", "================================================================================\n" ] } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 9: Fill Missing Required Skills via Keyword Matching\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "print(\"๐Ÿ” FILLING MISSING REQUIRED SKILLS...\")\n", "print(\"=\" * 80)\n", "\n", "# Load skills reference\n", "skills_ref = pd.read_csv(f'{Config.CSV_PATH}skills.csv')\n", "skill_names = set(skills_ref['skill_name'].str.lower().unique())\n", "\n", "print(f\"โœ… Loaded {len(skill_names):,} unique skills\")\n", "\n", "# Find companies with empty required_skills\n", "empty_mask = (companies_full['required_skills'] == 'Not specified') | \\\n", " (companies_full['required_skills'].isna())\n", "empty_count = empty_mask.sum()\n", "\n", "print(f\"๐Ÿ” Found {empty_count:,} companies with missing skills\")\n", "\n", "if empty_count > 0:\n", " print(f\"\\n๐Ÿ”„ Extracting skills from job postings text...\")\n", " \n", " # Get postings for companies with empty skills\n", " empty_companies = companies_full[empty_mask]['company_id'].tolist()\n", " relevant_postings = postings[postings['company_id'].isin(empty_companies)].copy()\n", " \n", " print(f\" Processing {len(relevant_postings):,} job postings...\")\n", " \n", " # Extract skills from description\n", " def extract_skills_from_text(text):\n", " if pd.isna(text):\n", " return []\n", " \n", " text_lower = str(text).lower()\n", " found_skills = []\n", " \n", " for skill in skill_names:\n", " if skill in text_lower:\n", " found_skills.append(skill)\n", " \n", " return found_skills\n", " \n", " # Extract from description column\n", " relevant_postings['extracted_skills'] = relevant_postings['description'].apply(extract_skills_from_text)\n", " \n", " # Aggregate by company\n", " skills_extracted = relevant_postings.groupby('company_id')['extracted_skills'].apply(\n", " lambda x: ', '.join(set([skill for sublist in x for skill in sublist]))\n", " ).reset_index()\n", " skills_extracted.columns = ['company_id', 'extracted_skills']\n", " \n", " # Update companies_full\n", " for idx, row in skills_extracted.iterrows():\n", " comp_id = row['company_id']\n", " extracted = row['extracted_skills']\n", " \n", " if extracted: # Only update if we found skills\n", " mask = companies_full['company_id'] == comp_id\n", " companies_full.loc[mask, 'required_skills'] = extracted\n", " \n", " # Final check\n", " still_empty = ((companies_full['required_skills'] == 'Not specified') | \n", " (companies_full['required_skills'].isna())).sum()\n", " \n", " filled = empty_count - still_empty\n", " \n", " print(f\"\\nโœ… RESULTS:\")\n", " print(f\" Filled: {filled:,} companies\")\n", " print(f\" Still empty: {still_empty:,} companies\")\n", " print(f\" Success rate: {(filled/empty_count*100):.1f}%\")\n", "\n", "else:\n", " print(\"โœ… No missing skills to fill!\")\n", "\n", "print(\"\\n\" + \"=\" * 80)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” VALIDATING JOB POSTING ENRICHMENT...\n", "================================================================================\n", "\n", "๐Ÿ“Š COVERAGE:\n", " Total companies: 24,473\n", " With postings: 24,473\n", " Without postings: 0\n", " Coverage: 100.0%\n", "\n", "๐Ÿ“‹ SAMPLE COMPANIES (random 5):\n", "--------------------------------------------------------------------------------\n", "\n", "๐Ÿข PulsePoint\n", " Total Postings: 5\n", " Industries: Advertising Services...\n", " Required Skills: Product Management, Customer Service, Advertising, Other, Sales, Analyst...\n", " Job Titles: Senior Product Manager, DSP, Account Manager, SaaS/Health Data/Analytics (Signal...\n", "\n", "๐Ÿข UFCW\n", " Total Postings: 2\n", " Industries: Non-profit Organizations...\n", " Required Skills: Information Technology, Design, Art/Creative, Information Technology...\n", " Job Titles: Records Manager, Digital Ads Specialist...\n", "\n", "๐Ÿข Solo Printing, LLC \n", " Total Postings: 1\n", " Industries: Printing Services...\n", " Required Skills: Sales, Business Development...\n", " Job Titles: Junior Account Executive (Sales)...\n", "\n", "๐Ÿข Franklin Street\n", " Total Postings: 2\n", " Industries: Real Estate...\n", " Required Skills: Accounting/Auditing, Finance, Business Development, Sales...\n", " Job Titles: Accounting Intern, Client Services Coordinator...\n", "\n", "๐Ÿข Sonic Automotive\n", " Total Postings: 7\n", " Industries: Motor Vehicle Manufacturing...\n", " Required Skills: Other, Customer Service...\n", " Job Titles: Parts Counterperson - Baytown Ford, Service BDC Associate - Carson Honda, Parts ...\n", "\n", "\n", "๐Ÿ” ENRICHMENT QUALITY CHECK:\n", "--------------------------------------------------------------------------------\n", "industries_list Filled: 24,365 ( 99.6%) Empty: 108\n", "specialties_list Filled: 17,780 ( 72.7%) Empty: 6,693\n", "required_skills Filled: 24,473 (100.0%) Empty: 0\n", "posted_job_titles Filled: 24,472 (100.0%) Empty: 1\n", "\n", "================================================================================\n", "\n", "๐ŸŽฏ CONCLUSION:\n", " โœ… If 'Filled' percentages are high โ†’ Enrichment working!\n", " โŒ If 'Empty' counts are high โ†’ Need to fix enrichment\n" ] } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# VALIDATION: Check Job Posting Enrichment\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "print(\"๐Ÿ” VALIDATING JOB POSTING ENRICHMENT...\")\n", "print(\"=\" * 80)\n", "\n", "# Stats\n", "print(f\"\\n๐Ÿ“Š COVERAGE:\")\n", "print(f\" Total companies: {len(companies_full):,}\")\n", "print(f\" With postings: {(companies_full['total_postings'] > 0).sum():,}\")\n", "print(f\" Without postings: {(companies_full['total_postings'] == 0).sum():,}\")\n", "print(f\" Coverage: {(companies_full['total_postings'] > 0).sum() / len(companies_full) * 100:.1f}%\")\n", "\n", "# Sample companies\n", "sample = companies_full.sample(5, random_state=42)\n", "\n", "print(\"\\n๐Ÿ“‹ SAMPLE COMPANIES (random 5):\")\n", "print(\"-\" * 80)\n", "\n", "for idx, row in sample.iterrows():\n", " print(f\"\\n๐Ÿข {row['name']}\")\n", " print(f\" Total Postings: {row['total_postings']}\")\n", " print(f\" Industries: {str(row['industries_list'])[:80]}...\")\n", " print(f\" Required Skills: {str(row['required_skills'])[:80]}...\")\n", " print(f\" Job Titles: {str(row['posted_job_titles'])[:80]}...\")\n", "\n", "# Check if enrichment columns exist and are populated\n", "print(\"\\n\\n๐Ÿ” ENRICHMENT QUALITY CHECK:\")\n", "print(\"-\" * 80)\n", "\n", "enrichment_cols = ['industries_list', 'specialties_list', 'required_skills', 'posted_job_titles']\n", "\n", "for col in enrichment_cols:\n", " empty = (companies_full[col] == 'Not specified') | (companies_full[col] == 'Various') | (companies_full[col] == 'General')\n", " empty_count = empty.sum()\n", " filled_count = len(companies_full) - empty_count\n", " \n", " print(f\"{col:25s} Filled: {filled_count:>6,} ({filled_count/len(companies_full)*100:>5.1f}%) Empty: {empty_count:>6,}\")\n", "\n", "print(\"\\n\" + \"=\" * 80)\n", "print(\"\\n๐ŸŽฏ CONCLUSION:\")\n", "print(\" โœ… If 'Filled' percentages are high โ†’ Enrichment working!\")\n", "print(\" โŒ If 'Empty' counts are high โ†’ Need to fix enrichment\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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company_idnamedescriptioncompany_sizestatecountrycityzip_codeaddressurlindustries_listspecialties_listposted_job_titlesrequired_skillsavg_med_salaryavg_max_salarytotal_postings
01009IBMAt IBM, we do more than work. We create. We cr...7.0NYUSArmonk, New York10504International Business Machines Corp.https://www.linkedin.com/company/ibmIT Services and IT ConsultingCloud, Mobile, Cognitive, Security, Research, ...Business Sales & Delivery Executive - SAP, Pro...Information Technology, Product Management, Ot...0.0182095.90625033
11016GE HealthCareEvery day millions of people feel the impact o...7.00USChicago0-https://www.linkedin.com/company/gehealthcareHospitals and Health CareHealthcare, BiotechnologyVP of Engineering, Demand Planning Leader - MR...Engineering, Information Technology, Other, Pr...0.0232626.22222253
21025Hewlett Packard EnterpriseOfficial LinkedIn of Hewlett Packard Enterpris...7.0TexasUSHouston773891701 E Mossy Oaks Rd Springhttps://www.linkedin.com/company/hewlett-packa...IT Services and IT ConsultingNot specifiedFederal IT Call Center Technician (TS/SCI, Ful...Information Technology, Project Management, In...0.0208231.45454514
31028OracleWeโ€™re a cloud technology company that provides...7.0TexasUSAustin787412300 Oracle Wayhttps://www.linkedin.com/company/oracleIT Services and IT Consultingenterprise, software, applications, database, ...Associate, Corporate Development, Customer Suc...Business Development, Sales, Other, Research, ...0.0122895.06929893
41033AccentureAccenture is a leading global professional ser...7.00IEDublin 20Grand Canal Harbourhttps://www.linkedin.com/company/accentureBusiness Consulting and ServicesManagement Consulting, Systems Integration and...Workday Certified Project Manager โ€“ Midwest MU...Strategy/Planning, Information Technology, Str...0.0216110.26666720
\n", "
" ], "text/plain": [ " company_id name \\\n", "0 1009 IBM \n", "1 1016 GE HealthCare \n", "2 1025 Hewlett Packard Enterprise \n", "3 1028 Oracle \n", "4 1033 Accenture \n", "\n", " description company_size state \\\n", "0 At IBM, we do more than work. We create. We cr... 7.0 NY \n", "1 Every day millions of people feel the impact o... 7.0 0 \n", "2 Official LinkedIn of Hewlett Packard Enterpris... 7.0 Texas \n", "3 Weโ€™re a cloud technology company that provides... 7.0 Texas \n", "4 Accenture is a leading global professional ser... 7.0 0 \n", "\n", " country city zip_code address \\\n", "0 US Armonk, New York 10504 International Business Machines Corp. \n", "1 US Chicago 0 - \n", "2 US Houston 77389 1701 E Mossy Oaks Rd Spring \n", "3 US Austin 78741 2300 Oracle Way \n", "4 IE Dublin 2 0 Grand Canal Harbour \n", "\n", " url \\\n", "0 https://www.linkedin.com/company/ibm \n", "1 https://www.linkedin.com/company/gehealthcare \n", "2 https://www.linkedin.com/company/hewlett-packa... \n", "3 https://www.linkedin.com/company/oracle \n", "4 https://www.linkedin.com/company/accenture \n", "\n", " industries_list \\\n", "0 IT Services and IT Consulting \n", "1 Hospitals and Health Care \n", "2 IT Services and IT Consulting \n", "3 IT Services and IT Consulting \n", "4 Business Consulting and Services \n", "\n", " specialties_list \\\n", "0 Cloud, Mobile, Cognitive, Security, Research, ... \n", "1 Healthcare, Biotechnology \n", "2 Not specified \n", "3 enterprise, software, applications, database, ... \n", "4 Management Consulting, Systems Integration and... \n", "\n", " posted_job_titles \\\n", "0 Business Sales & Delivery Executive - SAP, Pro... \n", "1 VP of Engineering, Demand Planning Leader - MR... \n", "2 Federal IT Call Center Technician (TS/SCI, Ful... \n", "3 Associate, Corporate Development, Customer Suc... \n", "4 Workday Certified Project Manager โ€“ Midwest MU... \n", "\n", " required_skills avg_med_salary \\\n", "0 Information Technology, Product Management, Ot... 0.0 \n", "1 Engineering, Information Technology, Other, Pr... 0.0 \n", "2 Information Technology, Project Management, In... 0.0 \n", "3 Business Development, Sales, Other, Research, ... 0.0 \n", "4 Strategy/Planning, Information Technology, Str... 0.0 \n", "\n", " avg_max_salary total_postings \n", "0 182095.906250 33 \n", "1 232626.222222 53 \n", "2 208231.454545 14 \n", "3 122895.069298 93 \n", "4 216110.266667 20 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "companies_full.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "๐Ÿ” DUPLICATE DETECTION REPORT\n", "================================================================================\n", "\n", "โ”Œโ”€ ๐Ÿ“Š resume_data.csv (Candidates)\n", "โ”‚ Primary Key: Resume_ID\n", "โ”‚ Total rows: 9,544\n", "โ”‚ Unique rows: 9,544\n", "โ”‚ Duplicates: 0\n", "โ”‚ Status: โœ… CLEAN\n", "โ””โ”€\n", "\n", "โ”Œโ”€ ๐Ÿ“Š companies.csv (Companies Base)\n", "โ”‚ Primary Key: company_id\n", "โ”‚ Total rows: 24,473\n", "โ”‚ Unique rows: 24,473\n", "โ”‚ Duplicates: 0\n", "โ”‚ Status: โœ… CLEAN\n", "โ””โ”€\n", "\n", "โ”Œโ”€ ๐Ÿ“Š company_industries.csv\n", "โ”‚ Primary Key: company_id + industry\n", "โ”‚ Total rows: 24,375\n", "โ”‚ Unique rows: 24,375\n", "โ”‚ Duplicates: 0\n", "โ”‚ Status: โœ… CLEAN\n", "โ””โ”€\n", "\n", "โ”Œโ”€ ๐Ÿ“Š company_specialities.csv\n", "โ”‚ Primary Key: company_id + speciality\n", "โ”‚ Total rows: 169,387\n", "โ”‚ Unique rows: 169,387\n", "โ”‚ Duplicates: 0\n", "โ”‚ Status: โœ… CLEAN\n", "โ””โ”€\n", "\n", "โ”Œโ”€ ๐Ÿ“Š employee_counts.csv\n", "โ”‚ Primary Key: company_id\n", "โ”‚ Total rows: 35,787\n", "โ”‚ Unique rows: 24,473\n", "โ”‚ Duplicates: 11,314\n", "โ”‚ Status: ๐Ÿ”ด HAS DUPLICATES\n", "โ””โ”€\n", "\n", "โ”Œโ”€ ๐Ÿ“Š postings.csv (Job Postings)\n", "โ”‚ Primary Key: job_id\n", "โ”‚ Total rows: 123,849\n", "โ”‚ Unique rows: 123,849\n", "โ”‚ Duplicates: 0\n", "โ”‚ Status: โœ… CLEAN\n", "โ””โ”€\n", "\n", "โ”Œโ”€ ๐Ÿ“Š companies_full (After Enrichment)\n", "โ”‚ Primary Key: company_id\n", "โ”‚ Total rows: 24,473\n", "โ”‚ Unique rows: 24,473\n", "โ”‚ Duplicates: 0\n", "โ”‚ Status: โœ… CLEAN\n", "โ””โ”€\n", "\n", "================================================================================\n", "๐Ÿ“Š SUMMARY\n", "================================================================================\n", "\n", "โœ… Clean datasets: 6/7\n", "๐Ÿ”ด Datasets with duplicates: 1/7\n", "๐Ÿ—‘๏ธ Total duplicates found: 11,314 rows\n", "\n", "โš ๏ธ DUPLICATES DETECTED!\n", "================================================================================\n" ] } ], "source": [ "## ๐Ÿ” Data Quality Check - Duplicate Detection\n", "\n", "\"\"\"\n", "Checking for duplicates in all datasets based on primary keys.\n", "This cell only REPORTS duplicates, does not modify data.\n", "\"\"\"\n", "\n", "print(\"=\" * 80)\n", "print(\"๐Ÿ” DUPLICATE DETECTION REPORT\")\n", "print(\"=\" * 80)\n", "print()\n", "\n", "# Define primary keys for each dataset\n", "duplicate_report = []\n", "\n", "# 1. Candidates\n", "print(\"โ”Œโ”€ ๐Ÿ“Š resume_data.csv (Candidates)\")\n", "print(f\"โ”‚ Primary Key: Resume_ID\")\n", "cand_total = len(candidates)\n", "cand_unique = candidates['Resume_ID'].nunique() if 'Resume_ID' in candidates.columns else len(candidates)\n", "cand_dups = cand_total - cand_unique\n", "print(f\"โ”‚ Total rows: {cand_total:,}\")\n", "print(f\"โ”‚ Unique rows: {cand_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {cand_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if cand_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Candidates', cand_total, cand_unique, cand_dups))\n", "\n", "# 2. Companies Base\n", "print(\"โ”Œโ”€ ๐Ÿ“Š companies.csv (Companies Base)\")\n", "print(f\"โ”‚ Primary Key: company_id\")\n", "comp_total = len(companies_base)\n", "comp_unique = companies_base['company_id'].nunique()\n", "comp_dups = comp_total - comp_unique\n", "print(f\"โ”‚ Total rows: {comp_total:,}\")\n", "print(f\"โ”‚ Unique rows: {comp_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {comp_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if comp_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "if comp_dups > 0:\n", " dup_ids = companies_base[companies_base.duplicated('company_id', keep=False)]['company_id'].value_counts().head(3)\n", " print(f\"โ”‚ Top duplicates:\")\n", " for cid, count in dup_ids.items():\n", " print(f\"โ”‚ - company_id={cid}: {count} times\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Companies Base', comp_total, comp_unique, comp_dups))\n", "\n", "# 3. Company Industries\n", "print(\"โ”Œโ”€ ๐Ÿ“Š company_industries.csv\")\n", "print(f\"โ”‚ Primary Key: company_id + industry\")\n", "ci_total = len(company_industries)\n", "ci_unique = len(company_industries.drop_duplicates(subset=['company_id', 'industry']))\n", "ci_dups = ci_total - ci_unique\n", "print(f\"โ”‚ Total rows: {ci_total:,}\")\n", "print(f\"โ”‚ Unique rows: {ci_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {ci_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if ci_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Company Industries', ci_total, ci_unique, ci_dups))\n", "\n", "# 4. Company Specialties\n", "print(\"โ”Œโ”€ ๐Ÿ“Š company_specialities.csv\")\n", "print(f\"โ”‚ Primary Key: company_id + speciality\")\n", "cs_total = len(company_specialties)\n", "cs_unique = len(company_specialties.drop_duplicates(subset=['company_id', 'speciality']))\n", "cs_dups = cs_total - cs_unique\n", "print(f\"โ”‚ Total rows: {cs_total:,}\")\n", "print(f\"โ”‚ Unique rows: {cs_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {cs_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if cs_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Company Specialties', cs_total, cs_unique, cs_dups))\n", "\n", "# 5. Employee Counts\n", "print(\"โ”Œโ”€ ๐Ÿ“Š employee_counts.csv\")\n", "print(f\"โ”‚ Primary Key: company_id\")\n", "ec_total = len(employee_counts)\n", "ec_unique = employee_counts['company_id'].nunique()\n", "ec_dups = ec_total - ec_unique\n", "print(f\"โ”‚ Total rows: {ec_total:,}\")\n", "print(f\"โ”‚ Unique rows: {ec_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {ec_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if ec_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Employee Counts', ec_total, ec_unique, ec_dups))\n", "\n", "# 6. Postings\n", "print(\"โ”Œโ”€ ๐Ÿ“Š postings.csv (Job Postings)\")\n", "print(f\"โ”‚ Primary Key: job_id\")\n", "if 'job_id' in postings.columns:\n", " post_total = len(postings)\n", " post_unique = postings['job_id'].nunique()\n", " post_dups = post_total - post_unique\n", "else:\n", " post_total = len(postings)\n", " post_unique = len(postings.drop_duplicates())\n", " post_dups = post_total - post_unique\n", "print(f\"โ”‚ Total rows: {post_total:,}\")\n", "print(f\"โ”‚ Unique rows: {post_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {post_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if post_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Postings', post_total, post_unique, post_dups))\n", "\n", "# 7. Companies Full (After Merge)\n", "print(\"โ”Œโ”€ ๐Ÿ“Š companies_full (After Enrichment)\")\n", "print(f\"โ”‚ Primary Key: company_id\")\n", "cf_total = len(companies_full)\n", "cf_unique = companies_full['company_id'].nunique()\n", "cf_dups = cf_total - cf_unique\n", "print(f\"โ”‚ Total rows: {cf_total:,}\")\n", "print(f\"โ”‚ Unique rows: {cf_unique:,}\")\n", "print(f\"โ”‚ Duplicates: {cf_dups:,}\")\n", "print(f\"โ”‚ Status: {'โœ… CLEAN' if cf_dups == 0 else '๐Ÿ”ด HAS DUPLICATES'}\")\n", "if cf_dups > 0:\n", " dup_ids = companies_full[companies_full.duplicated('company_id', keep=False)]['company_id'].value_counts().head(5)\n", " print(f\"โ”‚\")\n", " print(f\"โ”‚ Top duplicate company_ids:\")\n", " for cid, count in dup_ids.items():\n", " comp_name = companies_full[companies_full['company_id'] == cid]['name'].iloc[0]\n", " print(f\"โ”‚ - {cid} ({comp_name}): {count} times\")\n", "print(\"โ””โ”€\\n\")\n", "duplicate_report.append(('Companies Full', cf_total, cf_unique, cf_dups))\n", "\n", "# Summary\n", "print(\"=\" * 80)\n", "print(\"๐Ÿ“Š SUMMARY\")\n", "print(\"=\" * 80)\n", "print()\n", "\n", "total_dups = sum(r[3] for r in duplicate_report)\n", "clean_datasets = sum(1 for r in duplicate_report if r[3] == 0)\n", "dirty_datasets = len(duplicate_report) - clean_datasets\n", "\n", "print(f\"โœ… Clean datasets: {clean_datasets}/{len(duplicate_report)}\")\n", "print(f\"๐Ÿ”ด Datasets with duplicates: {dirty_datasets}/{len(duplicate_report)}\")\n", "print(f\"๐Ÿ—‘๏ธ Total duplicates found: {total_dups:,} rows\")\n", "print()\n", "\n", "if dirty_datasets > 0:\n", " print(\"โš ๏ธ DUPLICATES DETECTED!\")\n", "else:\n", " print(\"โœ… All datasets are clean! No duplicates found.\")\n", "\n", "print(\"=\" * 80)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 12a: Load Embedding Model & Pre-computed Vectors" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿง  Loading embedding model...\n", "\n", "โœ… Model loaded: all-MiniLM-L6-v2\n", "๐Ÿ“ Embedding dimension: โ„^384\n", "\n", "๐Ÿ“‚ Loading pre-computed embeddings...\n", "โœ… Loaded from ../processed/\n", "๐Ÿ“Š Candidate vectors: (9544, 384)\n", "๐Ÿ“Š Company vectors: (24473, 384)\n", "\n" ] } ], "source": [ "print(\"๐Ÿง  Loading embedding model...\\n\")\n", "model = SentenceTransformer(Config.EMBEDDING_MODEL)\n", "embedding_dim = model.get_sentence_embedding_dimension()\n", "print(f\"โœ… Model loaded: {Config.EMBEDDING_MODEL}\")\n", "print(f\"๐Ÿ“ Embedding dimension: โ„^{embedding_dim}\\n\")\n", "\n", "print(\"๐Ÿ“‚ Loading pre-computed embeddings...\")\n", "\n", "try:\n", " # Try to load from processed folder\n", " cand_vectors = np.load(f'{Config.PROCESSED_PATH}candidate_embeddings.npy')\n", " comp_vectors = np.load(f'{Config.PROCESSED_PATH}company_embeddings.npy')\n", " \n", " print(f\"โœ… Loaded from {Config.PROCESSED_PATH}\")\n", " print(f\"๐Ÿ“Š Candidate vectors: {cand_vectors.shape}\")\n", " print(f\"๐Ÿ“Š Company vectors: {comp_vectors.shape}\\n\")\n", " \n", "except FileNotFoundError:\n", " print(\"โš ๏ธ Pre-computed embeddings not found!\")\n", " print(\" Embeddings will need to be generated (takes ~5-10 minutes)\")\n", " print(\" This is normal if running for the first time.\\n\")\n", " \n", " # You can add embedding generation code here if needed\n", " # For now, we'll skip to keep notebook clean\n", " cand_vectors = None\n", " comp_vectors = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 12b: Generate Embeddings & Pre-computed Vectors" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# #last time running:\n", "# from datetime import datetime\n", "# print(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# # CELL 9: Generate Embeddings (CPU ONLY)\n", "# # โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "# print(\"๐Ÿง  GENERATING EMBEDDINGS...\")\n", "# print(\"=\" * 80)\n", "\n", "# print(f\"\\n๐Ÿ”ง Loading model: {Config.EMBEDDING_MODEL} (CPU)\")\n", "# model = SentenceTransformer(Config.EMBEDDING_MODEL, device='cpu')\n", "# print(f\"โœ… Loaded! Dim: {model.get_sentence_embedding_dimension()}\")\n", "\n", "# # ============================================================================\n", "# # CANDIDATES\n", "# # ============================================================================\n", "# print(f\"\\n1๏ธโƒฃ CANDIDATES ({len(candidates):,})\")\n", "\n", "# cand_builder = CandidateTextBuilder()\n", "# candidate_texts = cand_builder.build_batch(candidates)\n", "\n", "# cand_vectors = model.encode(\n", "# candidate_texts,\n", "# show_progress_bar=True,\n", "# batch_size=16,\n", "# normalize_embeddings=True,\n", "# convert_to_numpy=True\n", "# )\n", "\n", "# print(f\"โœ… Shape: {cand_vectors.shape}\")\n", "# np.save(f'{Config.PROCESSED_PATH}candidate_embeddings.npy', cand_vectors)\n", "# candidates.to_pickle(f'{Config.PROCESSED_PATH}candidates_metadata.pkl')\n", "# print(f\"๐Ÿ’พ Saved\")\n", "\n", "# # ============================================================================\n", "# # COMPANIES\n", "# # ============================================================================\n", "# print(f\"\\n2๏ธโƒฃ COMPANIES ({len(companies_full):,})\")\n", "\n", "# comp_builder = CompanyTextBuilder()\n", "# company_texts = comp_builder.build_batch(companies_full)\n", "\n", "# comp_vectors = model.encode(\n", "# company_texts,\n", "# show_progress_bar=True,\n", "# batch_size=16,\n", "# normalize_embeddings=True,\n", "# convert_to_numpy=True\n", "# )\n", "\n", "# print(f\"โœ… Shape: {comp_vectors.shape}\")\n", "# np.save(f'{Config.PROCESSED_PATH}company_embeddings.npy', comp_vectors)\n", "# companies_full.to_pickle(f'{Config.PROCESSED_PATH}companies_metadata.pkl')\n", "# print(f\"๐Ÿ’พ Saved\")\n", "\n", "# # ============================================================================\n", "# # DONE\n", "# # ============================================================================\n", "# print(f\"\\n{'='*80}\")\n", "# print(f\"๐ŸŽฏ DONE!\")\n", "# print(f\"Candidates: {cand_vectors.shape}\")\n", "# print(f\"Companies: {comp_vectors.shape}\")\n", "# print(f\"{'='*80}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 8: Core Matching Function" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Safe matching function loaded!\n", "\n", "๐Ÿ“Š DIAGNOSTICS:\n", " Candidate vectors: 9,544\n", " Company vectors: 24,473\n", " Companies dataset: 24,473\n", "\n", "โœ… Embeddings and dataset are aligned!\n" ] } ], "source": [ "# ============================================================================\n", "# CORE MATCHING FUNCTION (SAFE VERSION)\n", "# ============================================================================\n", "\n", "def find_top_matches(candidate_idx: int, top_k: int = 10) -> list:\n", " \"\"\"\n", " Find top K company matches for a candidate.\n", " \n", " SAFE VERSION: Handles index mismatches between embeddings and dataset\n", " \n", " Args:\n", " candidate_idx: Index of candidate in candidates DataFrame\n", " top_k: Number of top matches to return\n", " \n", " Returns:\n", " List of tuples: [(company_idx, similarity_score), ...]\n", " \"\"\"\n", " \n", " # Validate candidate index\n", " if candidate_idx >= len(cand_vectors):\n", " print(f\"โŒ Candidate index {candidate_idx} out of range\")\n", " return []\n", " \n", " # Get candidate vector\n", " cand_vec = cand_vectors[candidate_idx].reshape(1, -1)\n", " \n", " # Calculate similarities with all company vectors\n", " similarities = cosine_similarity(cand_vec, comp_vectors)[0]\n", " \n", " # CRITICAL FIX: Only use indices that exist in companies_full\n", " max_valid_idx = len(companies_full) - 1\n", " \n", " # Truncate similarities to valid range\n", " valid_similarities = similarities[:max_valid_idx + 1]\n", " \n", " # Get top K indices from valid range\n", " top_indices = np.argsort(valid_similarities)[::-1][:top_k]\n", " \n", " # Return (index, score) tuples\n", " results = [(int(idx), float(valid_similarities[idx])) for idx in top_indices]\n", " \n", " return results\n", "\n", "# Test function and show diagnostics\n", "print(\"โœ… Safe matching function loaded!\")\n", "print(f\"\\n๐Ÿ“Š DIAGNOSTICS:\")\n", "print(f\" Candidate vectors: {len(cand_vectors):,}\")\n", "print(f\" Company vectors: {len(comp_vectors):,}\")\n", "print(f\" Companies dataset: {len(companies_full):,}\")\n", "\n", "if len(comp_vectors) > len(companies_full):\n", " print(f\"\\nโš ๏ธ INDEX MISMATCH DETECTED!\")\n", " print(f\" Embeddings: {len(comp_vectors):,}\")\n", " print(f\" Dataset: {len(companies_full):,}\")\n", " print(f\" Missing rows: {len(comp_vectors) - len(companies_full):,}\")\n", " print(f\"\\n๐Ÿ’ก CAUSE: Embeddings generated BEFORE deduplication\")\n", " print(f\"\\n๐ŸŽฏ SOLUTIONS:\")\n", " print(f\" A. Safe functions active (current) โœ…\")\n", " print(f\" B. Regenerate embeddings after dedup\")\n", " print(f\" C. Run collaborative filtering step\")\n", "else:\n", " print(f\"\\nโœ… Embeddings and dataset are aligned!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 9: Initialize FREE LLM (Hugging Face)\n", "\n", "### Get your FREE token: https://huggingface.co/settings/tokens" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Hugging Face client initialized (FREE)\n", "๐Ÿค– Model: meta-llama/Llama-3.2-3B-Instruct\n", "๐Ÿ’ฐ Cost: $0.00 (completely free!)\n", "\n", "โœ… LLM helper functions ready\n" ] } ], "source": [ "# Initialize Hugging Face Inference Client (FREE)\n", "if Config.HF_TOKEN:\n", " try:\n", " hf_client = InferenceClient(token=Config.HF_TOKEN)\n", " print(\"โœ… Hugging Face client initialized (FREE)\")\n", " print(f\"๐Ÿค– Model: {Config.LLM_MODEL}\")\n", " print(\"๐Ÿ’ฐ Cost: $0.00 (completely free!)\\n\")\n", " LLM_AVAILABLE = True\n", " except Exception as e:\n", " print(f\"โš ๏ธ Failed to initialize HF client: {e}\")\n", " LLM_AVAILABLE = False\n", "else:\n", " print(\"โš ๏ธ No Hugging Face token configured\")\n", " print(\" LLM features will be disabled\")\n", " print(\"\\n๐Ÿ“ To enable:\")\n", " print(\" 1. Go to: https://huggingface.co/settings/tokens\")\n", " print(\" 2. Create a token (free)\")\n", " print(\" 3. Set: Config.HF_TOKEN = 'your-token-here'\\n\")\n", " LLM_AVAILABLE = False\n", " hf_client = None\n", "\n", "def call_llm(prompt: str, max_tokens: int = 1000) -> str:\n", " \"\"\"\n", " Generic LLM call using Hugging Face Inference API (FREE).\n", " \"\"\"\n", " if not LLM_AVAILABLE:\n", " return \"[LLM not available - check .env file for HF_TOKEN]\"\n", " \n", " try:\n", " response = hf_client.chat_completion( # โœ… chat_completion\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", " model=Config.LLM_MODEL,\n", " max_tokens=max_tokens,\n", " temperature=0.7\n", " )\n", " return response.choices[0].message.content # โœ… Extrai conteรบdo\n", " except Exception as e:\n", " return f\"[Error: {str(e)}]\"\n", "\n", "print(\"โœ… LLM helper functions ready\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 10: Pydantic Schemas for Structured Output" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Pydantic schemas defined\n" ] } ], "source": [ "class JobLevelClassification(BaseModel):\n", " \"\"\"Job level classification result\"\"\"\n", " level: Literal['Entry', 'Mid', 'Senior', 'Executive']\n", " confidence: float = Field(ge=0.0, le=1.0)\n", " reasoning: str\n", "\n", "class SkillsTaxonomy(BaseModel):\n", " \"\"\"Structured skills extraction\"\"\"\n", " technical_skills: List[str] = Field(default_factory=list)\n", " soft_skills: List[str] = Field(default_factory=list)\n", " certifications: List[str] = Field(default_factory=list)\n", " languages: List[str] = Field(default_factory=list)\n", "\n", "class MatchExplanation(BaseModel):\n", " \"\"\"Match reasoning\"\"\"\n", " overall_score: float = Field(ge=0.0, le=1.0)\n", " match_strengths: List[str]\n", " skill_gaps: List[str]\n", " recommendation: str\n", " fit_summary: str = Field(max_length=200)\n", "\n", "print(\"โœ… Pydantic schemas defined\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 11: Job Level Classification (Zero-Shot)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿงช Testing zero-shot classification...\n", "\n", "๐Ÿ“Š Classification Result:\n", "{\n", " \"level\": \"Mid\",\n", " \"confidence\": 0.85,\n", " \"reasoning\": \"3-5 years of experience indicated\"\n", "}\n" ] } ], "source": [ "def classify_job_level_zero_shot(job_description: str) -> Dict:\n", " \"\"\"\n", " Zero-shot job level classification.\n", " \n", " Returns classification as: Entry, Mid, Senior, or Executive\n", " \"\"\"\n", " \n", " prompt = f\"\"\"Classify this job posting into ONE seniority level.\n", "\n", "Levels:\n", "- Entry: 0-2 years experience, junior roles\n", "- Mid: 3-5 years experience, independent work\n", "- Senior: 6-10 years experience, technical leadership\n", "- Executive: 10+ years, strategic leadership, C-level\n", "\n", "Job Posting:\n", "{job_description[:500]}\n", "\n", "Return ONLY valid JSON:\n", "{{\n", " \"level\": \"Entry|Mid|Senior|Executive\",\n", " \"confidence\": 0.85,\n", " \"reasoning\": \"Brief explanation\"\n", "}}\n", "\"\"\"\n", " \n", " response = call_llm(prompt)\n", " \n", " try:\n", " # Extract JSON\n", " json_str = response.strip()\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " elif '```' in json_str:\n", " json_str = json_str.split('```')[1].split('```')[0].strip()\n", " \n", " # Find JSON in response\n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " result = json.loads(json_str)\n", " return result\n", " except:\n", " return {\n", " \"level\": \"Unknown\",\n", " \"confidence\": 0.0,\n", " \"reasoning\": \"Failed to parse response\"\n", " }\n", "\n", "# Test if LLM available and data loaded\n", "if LLM_AVAILABLE and len(postings) > 0:\n", " print(\"๐Ÿงช Testing zero-shot classification...\\n\")\n", " sample = postings.iloc[0]['description']\n", " result = classify_job_level_zero_shot(sample)\n", " \n", " print(\"๐Ÿ“Š Classification Result:\")\n", " print(json.dumps(result, indent=2))\n", "else:\n", " print(\"โš ๏ธ Skipped - LLM not available or no data\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 12: Few-Shot Learning" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Few-shot classifier (robust parsing)\n", "\n", "๐Ÿงช Comparing Zero-Shot vs Few-Shot...\n", "\n", "๐Ÿ“Š Comparison:\n", "Zero-shot: Entry (confidence: 0.85)\n", "Few-shot: Entry (confidence: 0.83)\n", "\n", "๐Ÿ” Few-shot reasoning: required experience in graphic design, no specific years mentioned...\n" ] } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# FEW-SHOT Job Level Classification (FIXED)\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "def classify_job_level_few_shot(job_description: str) -> Dict:\n", " \"\"\"Few-shot classification with robust parsing\"\"\"\n", " \n", " prompt = f\"\"\"Classify this job posting using examples.\n", "\n", "EXAMPLES:\n", "- \"Recent graduate wanted. Python basics.\" โ†’ Entry\n", "- \"5+ years backend. Lead team.\" โ†’ Senior \n", "- \"CTO position. 15+ years strategy.\" โ†’ Executive\n", "\n", "JOB POSTING:\n", "{job_description[:500]}\n", "\n", "IMPORTANT: Return ONLY valid JSON in this exact format:\n", "{{\"level\": \"Entry|Mid|Senior|Executive\", \"confidence\": 0.85, \"reasoning\": \"brief explanation\"}}\n", "\n", "Do not include any other text, markdown, or code blocks.\"\"\"\n", " \n", " response = call_llm(prompt, max_tokens=200)\n", " \n", " try:\n", " # Clean response\n", " json_str = response.strip()\n", " \n", " # Remove markdown if present\n", " if '```' in json_str:\n", " json_str = json_str.split('```json')[-1].split('```')[0].strip()\n", " if not json_str:\n", " json_str = response.split('```')[-2].strip()\n", " \n", " # Extract JSON object\n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " result = json.loads(json_str)\n", " \n", " # Validate fields\n", " if 'level' not in result:\n", " raise ValueError(\"Missing 'level' field\")\n", " \n", " # Ensure confidence exists\n", " if 'confidence' not in result:\n", " result['confidence'] = 0.85\n", " \n", " return result\n", " \n", " except Exception as e:\n", " # Fallback: try to extract level from raw text\n", " response_lower = response.lower()\n", " \n", " if 'entry' in response_lower or 'junior' in response_lower:\n", " level = 'Entry'\n", " elif 'senior' in response_lower:\n", " level = 'Senior'\n", " elif 'executive' in response_lower or 'c-level' in response_lower:\n", " level = 'Executive'\n", " elif 'mid' in response_lower:\n", " level = 'Mid'\n", " else:\n", " level = 'Unknown'\n", " \n", " return {\n", " \"level\": level,\n", " \"confidence\": 0.70 if level != 'Unknown' else 0.0,\n", " \"reasoning\": f\"Extracted from text (parse error: {str(e)[:50]})\"\n", " }\n", "\n", "print(\"โœ… Few-shot classifier (robust parsing)\")\n", "\n", "# Test comparison\n", "if LLM_AVAILABLE and len(postings) > 0:\n", " print(\"\\n๐Ÿงช Comparing Zero-Shot vs Few-Shot...\")\n", " sample = postings.iloc[0]['description']\n", " \n", " zero = classify_job_level_zero_shot(sample)\n", " few = classify_job_level_few_shot(sample)\n", " \n", " print(\"\\n๐Ÿ“Š Comparison:\")\n", " print(f\"Zero-shot: {zero['level']} (confidence: {zero['confidence']:.2f})\")\n", " print(f\"Few-shot: {few['level']} (confidence: {few['confidence']:.2f})\")\n", " \n", " print(f\"\\n๐Ÿ” Few-shot reasoning: {few['reasoning'][:100]}...\")\n", "else:\n", " print(\"โš ๏ธ LLM not available\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 13: Structured Skills Extraction" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "โœ… Skills extraction (fixed prompt)\n", "\n", "๐Ÿ” Testing skills extraction...\n", "\n", "๐Ÿ“„ Job posting sample:\n", " Job descriptionA leading real estate firm in New Jersey is seeking an administrative Marketing Coordinator with some experience in graphic design. You will be working closely with our fun, kind, ambit...\n", "\n", "๐Ÿ“Š Extracted Skills:\n", "{\n", " \"technical_skills\": [\n", " \"Adobe Creative Cloud\",\n", " \"Indesign\",\n", " \"Illustrator\",\n", " \"Photoshop\",\n", " \"Microsoft Office Suite\"\n", " ],\n", " \"soft_skills\": [\n", " \"teamwork\",\n", " \"communication\",\n", " \"problem-solving\",\n", " \"proactivity\",\n", " \"positivity\",\n", " \"responsibility\",\n", " \"respect\",\n", " \"cool-under-pressure\",\n", " \"kindness\",\n", " \"cooperation\",\n", " \"inclusion\",\n", " \"fantastic taste\"\n", " ],\n", " \"certifications\": [],\n", " \"languages\": []\n", "}\n", "\n", "โœ… Total skills found: 17\n" ] } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# FIXED: Skills Extraction (better prompt)\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "def extract_skills_taxonomy(job_description: str) -> Dict:\n", " \"\"\"Extract structured skills using LLM + Pydantic validation\"\"\"\n", " \n", " prompt = f\"\"\"Extract ALL skills mentioned in this job posting.\n", "\n", "JOB POSTING:\n", "{job_description[:800]}\n", "\n", "Analyze the text above and extract:\n", "- Technical skills (programming, tools, platforms)\n", "- Soft skills (teamwork, communication, problem-solving)\n", "- Certifications (if any)\n", "- Languages (if mentioned)\n", "\n", "Return ONLY valid JSON with actual skills found in the text:\n", "{{\n", " \"technical_skills\": [\"skill1\", \"skill2\"],\n", " \"soft_skills\": [\"skill1\", \"skill2\"],\n", " \"certifications\": [\"cert1\"],\n", " \"languages\": [\"lang1\"]\n", "}}\n", "\n", "IMPORTANT: \n", "- Extract ONLY skills that are ACTUALLY in the job posting above\n", "- If no skills found in a category, use empty array []\n", "- Do not include example values\n", "\"\"\"\n", " \n", " response = call_llm(prompt, max_tokens=800)\n", " \n", " try:\n", " json_str = response.strip()\n", " \n", " # Remove markdown\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " elif '```' in json_str:\n", " json_str = json_str.split('```')[1].split('```')[0].strip()\n", " \n", " # Extract JSON\n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " data = json.loads(json_str)\n", " \n", " # Validate with Pydantic\n", " validated = SkillsTaxonomy(**data)\n", " return validated.model_dump()\n", " \n", " except Exception as e:\n", " print(f\"โš ๏ธ Parse error: {e}\")\n", " return {\n", " \"technical_skills\": [],\n", " \"soft_skills\": [],\n", " \"certifications\": [],\n", " \"languages\": []\n", " }\n", "\n", "print(\"โœ… Skills extraction (fixed prompt)\")\n", "\n", "# Test\n", "if LLM_AVAILABLE and len(postings) > 0:\n", " print(\"\\n๐Ÿ” Testing skills extraction...\")\n", " sample = postings.iloc[0]['description']\n", " \n", " print(f\"\\n๐Ÿ“„ Job posting sample:\")\n", " print(f\" {sample[:200]}...\\n\")\n", " \n", " skills = extract_skills_taxonomy(sample)\n", " \n", " print(\"๐Ÿ“Š Extracted Skills:\")\n", " print(json.dumps(skills, indent=2))\n", " \n", " # Check if actually extracted something\n", " total_skills = sum(len(v) for v in skills.values())\n", " print(f\"\\n{'โœ…' if total_skills > 0 else 'โš ๏ธ '} Total skills found: {total_skills}\")\n", "else:\n", " print(\"โš ๏ธ LLM not available\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 14: Match Explainability" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ’ก Testing match explainability...\n", "\n", "๐Ÿ“Š Match Explanation:\n", "{\n", " \"overall_score\": 0.7105909585952759,\n", " \"match_strengths\": [\n", " \"Big Data expertise\"\n", " ],\n", " \"skill_gaps\": [\n", " \"Design experience\",\n", " \"Art/Creative experience\",\n", " \"No experience in Product Management, Marketing\"\n", " ],\n", " \"recommendation\": \"The candidate could consider gaining experience in Product Management, Marketing, and Art/Creative fields to fully align with the company's requirements.\",\n", " \"fit_summary\": \"A Big Data Analyst with relevant skills but lacking in other areas such as design and marketing.\"\n", "}\n" ] } ], "source": [ "def explain_match(candidate_idx: int, company_idx: int, similarity_score: float) -> Dict:\n", " \"\"\"\n", " Generate LLM explanation for why candidate matches company.\n", " \"\"\"\n", " \n", " cand = candidates.iloc[candidate_idx]\n", " comp = companies_full.iloc[company_idx]\n", " \n", " cand_skills = str(cand.get('skills', 'N/A'))[:300]\n", " cand_exp = str(cand.get('positions', 'N/A'))[:300]\n", " comp_req = str(comp.get('required_skills', 'N/A'))[:300]\n", " comp_name = comp.get('name', 'Unknown')\n", " \n", " prompt = f\"\"\"Explain why this candidate matches this company.\n", "\n", "Candidate:\n", "Skills: {cand_skills}\n", "Experience: {cand_exp}\n", "\n", "Company: {comp_name}\n", "Requirements: {comp_req}\n", "\n", "Similarity Score: {similarity_score:.2f}\n", "\n", "Return JSON:\n", "{{\n", " \"overall_score\": {similarity_score},\n", " \"match_strengths\": [\"Top 3-5 matching factors\"],\n", " \"skill_gaps\": [\"Missing skills\"],\n", " \"recommendation\": \"What candidate should do\",\n", " \"fit_summary\": \"One sentence summary\"\n", "}}\n", "\"\"\"\n", " \n", " response = call_llm(prompt, max_tokens=1000)\n", " \n", " try:\n", " json_str = response.strip()\n", " if '```json' in json_str:\n", " json_str = json_str.split('```json')[1].split('```')[0].strip()\n", " \n", " if '{' in json_str and '}' in json_str:\n", " start = json_str.index('{')\n", " end = json_str.rindex('}') + 1\n", " json_str = json_str[start:end]\n", " \n", " data = json.loads(json_str)\n", " return data\n", " except:\n", " return {\n", " \"overall_score\": similarity_score,\n", " \"match_strengths\": [\"Unable to generate\"],\n", " \"skill_gaps\": [],\n", " \"recommendation\": \"Review manually\",\n", " \"fit_summary\": f\"Match score: {similarity_score:.2f}\"\n", " }\n", "\n", "# Test explainability\n", "if LLM_AVAILABLE and cand_vectors is not None and len(candidates) > 0:\n", " print(\"๐Ÿ’ก Testing match explainability...\\n\")\n", " matches = find_top_matches(0, top_k=1)\n", " if matches:\n", " comp_idx, score = matches[0]\n", " explanation = explain_match(0, comp_idx, score)\n", " \n", " print(\"๐Ÿ“Š Match Explanation:\")\n", " print(json.dumps(explanation, indent=2))\n", "else:\n", " print(\"โš ๏ธ Skipped - requirements not met\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” MATCH QUALITY CHECK\n", "================================================================================\n", "\n", "Candidate 0:\n", " Category: N/A\n", " Skills: ['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++'...\n", "\n", "Top 3 Company Matches:\n", "\n", "1. Cloudera (score: 0.711)\n", " Industries: Software Development...\n", " Required Skills: Product Management, Marketing, Design, Art/Creative, Information Technology, Information Technology...\n", "\n", "2. Info Services (score: 0.644)\n", " Industries: IT Services and IT Consulting...\n", " Required Skills: Information Technology, Engineering, Consulting...\n", "\n", "3. CloudIngest (score: 0.640)\n", " Industries: Software Development...\n", " Required Skills: Human Resources, Engineering, Information Technology...\n", "\n", "================================================================================\n", "โ“ Do these matches make SEMANTIC SENSE?\n" ] } ], "source": [ "# Check if matches make semantic sense\n", "print(\"๐Ÿ” MATCH QUALITY CHECK\")\n", "print(\"=\" * 80)\n", "\n", "cand_0 = candidates.iloc[0]\n", "print(f\"\\nCandidate 0:\")\n", "print(f\" Category: {cand_0.get('Category', 'N/A')}\")\n", "print(f\" Skills: {str(cand_0.get('skills', 'N/A'))[:150]}...\")\n", "\n", "matches = find_top_matches(0, top_k=3)\n", "print(f\"\\nTop 3 Company Matches:\")\n", "for i, (comp_idx, score) in enumerate(matches, 1):\n", " comp = companies_full.iloc[comp_idx]\n", " print(f\"\\n{i}. {comp['name']} (score: {score:.3f})\")\n", " print(f\" Industries: {str(comp['industries_list'])[:100]}...\")\n", " print(f\" Required Skills: {str(comp['required_skills'])[:100]}...\")\n", "\n", "print(\"\\n\" + \"=\" * 80)\n", "print(\"โ“ Do these matches make SEMANTIC SENSE?\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 16: Detailed Match Visualization" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” DETAILED MATCH ANALYSIS\n", "====================================================================================================\n", "\n", "๐ŸŽฏ CANDIDATE #9543\n", "Resume ID: N/A\n", "Category: N/A\n", "Skills: ['assisted living', 'interpersonal and communication', 'insurance', 'internal medicine', 'managing', 'marketing', 'marketing/sales', 'meetings', 'ment...\n", "\n", "๐Ÿ”— TOP 5 MATCHES:\n", "\n", "#1. Confidential (Score: 0.6424)\n", " Industries: General...\n", "#2. Guthrie (Score: 0.6385)\n", " Industries: Hospitals and Health Care...\n", "#3. Jobot Consulting (Score: 0.6119)\n", " Industries: Business Consulting and Services...\n", "#4. A Hiring Company (Score: 0.6058)\n", " Industries: Book and Periodical Publishing...\n", "#5. IMCS (Score: 0.6043)\n", " Industries: Business Consulting and Services...\n", "\n", "====================================================================================================\n" ] }, { "data": { "text/plain": [ "[(16692, 0.6423980593681335),\n", " (7219, 0.6384985446929932),\n", " (23994, 0.6119470596313477),\n", " (24213, 0.6058148741722107),\n", " (9974, 0.6043329834938049)]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ============================================================================\n", "# ๐Ÿ” DETAILED MATCH EXAMPLE\n", "# ============================================================================\n", "\n", "def show_detailed_match_example(candidate_idx=0, top_k=5):\n", " print(\"๐Ÿ” DETAILED MATCH ANALYSIS\")\n", " print(\"=\" * 100)\n", " \n", " if candidate_idx >= len(candidates):\n", " print(f\"โŒ ERROR: Candidate {candidate_idx} out of range\")\n", " return None\n", " \n", " cand = candidates.iloc[candidate_idx]\n", " \n", " print(f\"\\n๐ŸŽฏ CANDIDATE #{candidate_idx}\")\n", " print(f\"Resume ID: {cand.get('Resume_ID', 'N/A')}\")\n", " print(f\"Category: {cand.get('Category', 'N/A')}\")\n", " print(f\"Skills: {str(cand.get('skills', 'N/A'))[:150]}...\\n\")\n", " \n", " matches = find_top_matches(candidate_idx, top_k=top_k)\n", " \n", " print(f\"๐Ÿ”— TOP {len(matches)} MATCHES:\\n\")\n", " \n", " for rank, (comp_idx, score) in enumerate(matches, 1):\n", " if comp_idx >= len(companies_full):\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " print(f\"#{rank}. {company.get('name', 'N/A')} (Score: {score:.4f})\")\n", " print(f\" Industries: {str(company.get('industries_list', 'N/A'))[:60]}...\")\n", " \n", " print(\"\\n\" + \"=\" * 100)\n", " return matches\n", "\n", "# Test\n", "show_detailed_match_example(candidate_idx=9543, top_k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 17: Bridging Concept Analysis" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐ŸŒ‰ THE BRIDGING CONCEPT\n", "==========================================================================================\n", "\n", "๐Ÿ“Š DATA REALITY:\n", " Total companies: 24,473\n", " WITH postings: 23,528 (96.1%)\n", " WITHOUT postings: 945\n", "\n", "๐ŸŽฏ THE PROBLEM:\n", " Companies: 'We are in TECH INDUSTRY'\n", " Candidates: 'I know PYTHON, AWS'\n", " โ†’ Different languages! ๐Ÿšซ\n", "\n", "๐ŸŒ‰ THE SOLUTION (BRIDGING):\n", " 1. Extract from postings: 'Need PYTHON developers'\n", " 2. Enrich company profile with skills\n", " 3. Now both speak SKILLS LANGUAGE! โœ…\n", "\n", "==========================================================================================\n" ] }, { "data": { "text/plain": [ "( company_id name \\\n", " 0 1009 IBM \n", " 1 1016 GE HealthCare \n", " 2 1025 Hewlett Packard Enterprise \n", " 3 1028 Oracle \n", " 4 1033 Accenture \n", " ... ... ... \n", " 24468 103463217 JRC Services \n", " 24469 103466352 Centent Consulting LLC \n", " 24470 103467540 Kings and Queens Productions, LLC \n", " 24471 103468936 WebUnite \n", " 24472 103472979 BlackVe \n", " \n", " description company_size \\\n", " 0 At IBM, we do more than work. We create. We cr... 7.0 \n", " 1 Every day millions of people feel the impact o... 7.0 \n", " 2 Official LinkedIn of Hewlett Packard Enterpris... 7.0 \n", " 3 Weโ€™re a cloud technology company that provides... 7.0 \n", " 4 Accenture is a leading global professional ser... 7.0 \n", " ... ... ... \n", " 24468 No description 2.0 \n", " 24469 Centent Consulting LLC is a reputable human re... NaN \n", " 24470 We are a small but mighty collection of thinke... NaN \n", " 24471 Our mission at WebUnite is to offer experience... NaN \n", " 24472 No description 1.0 \n", " \n", " state country city zip_code \\\n", " 0 NY US Armonk, New York 10504 \n", " 1 0 US Chicago 0 \n", " 2 Texas US Houston 77389 \n", " 3 Texas US Austin 78741 \n", " 4 0 IE Dublin 2 0 \n", " ... ... ... ... ... \n", " 24468 0 0 0 0 \n", " 24469 0 0 0 0 \n", " 24470 0 0 0 0 \n", " 24471 Pennsylvania US Southampton 18966 \n", " 24472 0 0 0 0 \n", " \n", " address \\\n", " 0 International Business Machines Corp. \n", " 1 - \n", " 2 1701 E Mossy Oaks Rd Spring \n", " 3 2300 Oracle Way \n", " 4 Grand Canal Harbour \n", " ... ... \n", " 24468 0 \n", " 24469 0 \n", " 24470 0 \n", " 24471 720 2nd Street Pike \n", " 24472 0 \n", " \n", " url \\\n", " 0 https://www.linkedin.com/company/ibm \n", " 1 https://www.linkedin.com/company/gehealthcare \n", " 2 https://www.linkedin.com/company/hewlett-packa... \n", " 3 https://www.linkedin.com/company/oracle \n", " 4 https://www.linkedin.com/company/accenture \n", " ... ... \n", " 24468 https://www.linkedin.com/company/jrcservices \n", " 24469 https://www.linkedin.com/company/centent-consu... \n", " 24470 https://www.linkedin.com/company/kings-and-que... \n", " 24471 https://www.linkedin.com/company/webunite \n", " 24472 https://www.linkedin.com/company/blackve \n", " \n", " industries_list \\\n", " 0 IT Services and IT Consulting \n", " 1 Hospitals and Health Care \n", " 2 IT Services and IT Consulting \n", " 3 IT Services and IT Consulting \n", " 4 Business Consulting and Services \n", " ... ... \n", " 24468 Facilities Services \n", " 24469 Business Consulting and Services \n", " 24470 Broadcast Media Production and Distribution \n", " 24471 Business Consulting and Services \n", " 24472 Defense and Space Manufacturing \n", " \n", " specialties_list \\\n", " 0 Cloud, Mobile, Cognitive, Security, Research, ... \n", " 1 Healthcare, Biotechnology \n", " 2 Not specified \n", " 3 enterprise, software, applications, database, ... \n", " 4 Management Consulting, Systems Integration and... \n", " ... ... \n", " 24468 Not specified \n", " 24469 Not specified \n", " 24470 Not specified \n", " 24471 Not specified \n", " 24472 Not specified \n", " \n", " posted_job_titles \\\n", " 0 Business Sales & Delivery Executive - SAP, Pro... \n", " 1 VP of Engineering, Demand Planning Leader - MR... \n", " 2 Federal IT Call Center Technician (TS/SCI, Ful... \n", " 3 Associate, Corporate Development, Customer Suc... \n", " 4 Workday Certified Project Manager โ€“ Midwest MU... \n", " ... ... \n", " 24468 Heating Air Conditioning Service Technician \n", " 24469 Cyber Security Officer \n", " 24470 New Business Developer \n", " 24471 Sales And Marketing Specialist \n", " 24472 Digital Integration & Supply Chain Director \n", " \n", " required_skills avg_med_salary \\\n", " 0 Information Technology, Product Management, Ot... 0.0 \n", " 1 Engineering, Information Technology, Other, Pr... 0.0 \n", " 2 Information Technology, Project Management, In... 0.0 \n", " 3 Business Development, Sales, Other, Research, ... 0.0 \n", " 4 Strategy/Planning, Information Technology, Str... 0.0 \n", " ... ... ... \n", " 24468 Management, Manufacturing 0.0 \n", " 24469 Engineering, Information Technology 0.0 \n", " 24470 Business Development, Sales 0.0 \n", " 24471 Sales, Business Development 0.0 \n", " 24472 Management, Manufacturing 0.0 \n", " \n", " avg_max_salary total_postings \n", " 0 182095.906250 33 \n", " 1 232626.222222 53 \n", " 2 208231.454545 14 \n", " 3 122895.069298 93 \n", " 4 216110.266667 20 \n", " ... ... ... \n", " 24468 0.000000 1 \n", " 24469 0.000000 1 \n", " 24470 0.000000 1 \n", " 24471 0.000000 1 \n", " 24472 0.000000 1 \n", " \n", " [23528 rows x 17 columns],\n", " company_id name \\\n", " 698 5022 University of Rochester Medical Center \n", " 764 5372 UC Santa Barbara \n", " 841 5830 Fairfax County Public Schools \n", " 1185 8235 Procom \n", " 1206 8363 Douglas Elliman Real Estate \n", " ... ... ... \n", " 24447 103407206 Wright Mindset Therapy Services, LLC \n", " 24448 103407882 Agricultural Office, Embassy of Chile in the US \n", " 24450 103410862 Bullseye Biosciences \n", " 24454 103414908 Somnio Wealth \n", " 24457 103426165 JPM Financial LLC \n", " \n", " description company_size \\\n", " 698 The University of Rochester Medical Center is ... 7.0 \n", " 764 UC Santa Barbara is consistently recognized fo... 6.0 \n", " 841 Fairfax County Public Schools (FCPS), located ... 7.0 \n", " 1185 Procom is one of North Americaโ€™s leading staff... 3.0 \n", " 1206 Douglas Elliman Inc. (NYSE: DOUG, โ€œDouglas Ell... 6.0 \n", " ... ... ... \n", " 24447 Wright Mindset Therapy Services, LLC is a prem... NaN \n", " 24448 No description NaN \n", " 24450 Bullseye Biosciences is a therapeutics discove... NaN \n", " 24454 Somnio Wealth is a Wealth Management Firm in L... NaN \n", " 24457 At JPM Financial, we aim to provide our client... NaN \n", " \n", " state country city zip_code \\\n", " 698 NY US Rochester 14642 \n", " 764 CA US Santa Barbara 93106 \n", " 841 Virginia US Falls Church 22042 \n", " 1185 Ontario CA Toronto M4S 2C6 \n", " 1206 NY US New York 10022 \n", " ... ... ... ... ... \n", " 24447 Maryland US Glen Burnie 21061 \n", " 24448 District of Columbia US Washington 20036 \n", " 24450 MA US Allston 02134 \n", " 24454 Kentucky US Louisville 40222 \n", " 24457 0 0 0 0 \n", " \n", " address \\\n", " 698 601 Elmwood Avenue \n", " 764 UC Santa Barbara \n", " 841 8115 Gatehouse Road \n", " 1185 2200 Yonge St. \n", " 1206 575 Madison Avenue \n", " ... ... \n", " 24447 0 \n", " 24448 1732 Massachusetts Ave NW \n", " 24450 Pagliuca Harvard Life Lab, 127 Western Ave \n", " 24454 0 \n", " 24457 0 \n", " \n", " url \\\n", " 698 https://www.linkedin.com/company/university-of... \n", " 764 https://www.linkedin.com/school/ucsantabarbara/ \n", " 841 https://www.linkedin.com/company/fairfax-count... \n", " 1185 https://www.linkedin.com/company/procom \n", " 1206 https://www.linkedin.com/company/douglaselliman \n", " ... ... \n", " 24447 https://www.linkedin.com/company/wright-mindse... \n", " 24448 https://www.linkedin.com/company/agricultural-... \n", " 24450 https://www.linkedin.com/company/bullseye-bios... \n", " 24454 https://www.linkedin.com/company/somnio-wealth \n", " 24457 https://www.linkedin.com/company/jpm-financial... \n", " \n", " industries_list \\\n", " 698 Hospitals and Health Care \n", " 764 Higher Education \n", " 841 Primary and Secondary Education \n", " 1185 IT Services and IT Consulting \n", " 1206 Real Estate \n", " ... ... \n", " 24447 Mental Health Care \n", " 24448 Government Administration \n", " 24450 Biotechnology Research \n", " 24454 Financial Services \n", " 24457 Telecommunications \n", " \n", " specialties_list \\\n", " 698 Education, Research, Wellness, Medical Care, M... \n", " 764 Education, Research , Service \n", " 841 education, k-12, elementary, secondary, teachi... \n", " 1185 Contract Staffing, Permanent Placement, Hybrid... \n", " 1206 Residential Sales, Residential Rentals, Commer... \n", " ... ... \n", " 24447 Not specified \n", " 24448 Not specified \n", " 24450 Not specified \n", " 24454 financial planning, wealth management, financi... \n", " 24457 Not specified \n", " \n", " posted_job_titles required_skills \\\n", " 698 Compliance Analyst II \n", " 764 Junior Specialist: Full-Time Research Software... \n", " 841 Instructional Assistant - Preschool Autism Cla... \n", " 1185 Electrical Planner \n", " 1206 Real Estate Office Assistant \n", " ... ... ... \n", " 24447 Clinical Supervisor \n", " 24448 Agriculture Specialist \n", " 24450 Scientist, Directed Evolution \n", " 24454 Director of First Impressions \n", " 24457 Remote Customer Service Agent \n", " \n", " avg_med_salary avg_max_salary total_postings \n", " 698 0.0 0.0 1 \n", " 764 0.0 65000.0 1 \n", " 841 0.0 0.0 1 \n", " 1185 0.0 0.0 1 \n", " 1206 0.0 0.0 1 \n", " ... ... ... ... \n", " 24447 0.0 0.0 1 \n", " 24448 0.0 0.0 1 \n", " 24450 0.0 0.0 1 \n", " 24454 0.0 0.0 1 \n", " 24457 0.0 55000.0 1 \n", " \n", " [945 rows x 17 columns])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ============================================================================\n", "# ๐ŸŒ‰ BRIDGING CONCEPT ANALYSIS\n", "# ============================================================================\n", "\n", "def show_bridging_concept_analysis():\n", " print(\"๐ŸŒ‰ THE BRIDGING CONCEPT\")\n", " print(\"=\" * 90)\n", " \n", " companies_with = companies_full[companies_full['required_skills'] != '']\n", " companies_without = companies_full[companies_full['required_skills'] == '']\n", " \n", " print(f\"\\n๐Ÿ“Š DATA REALITY:\")\n", " print(f\" Total companies: {len(companies_full):,}\")\n", " print(f\" WITH postings: {len(companies_with):,} ({len(companies_with)/len(companies_full)*100:.1f}%)\")\n", " print(f\" WITHOUT postings: {len(companies_without):,}\\n\")\n", " \n", " print(\"๐ŸŽฏ THE PROBLEM:\")\n", " print(\" Companies: 'We are in TECH INDUSTRY'\")\n", " print(\" Candidates: 'I know PYTHON, AWS'\")\n", " print(\" โ†’ Different languages! ๐Ÿšซ\\n\")\n", " \n", " print(\"๐ŸŒ‰ THE SOLUTION (BRIDGING):\")\n", " print(\" 1. Extract from postings: 'Need PYTHON developers'\")\n", " print(\" 2. Enrich company profile with skills\")\n", " print(\" 3. Now both speak SKILLS LANGUAGE! โœ…\\n\")\n", " \n", " print(\"=\" * 90)\n", " return companies_with, companies_without\n", "\n", "# Test\n", "show_bridging_concept_analysis()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” REQUIRED_SKILLS CHECK\n", "================================================================================\n", "\n", "Total companies: 24,473\n", "\n", "Value counts:\n", "required_skills\n", "Management, Manufacturing 1418\n", "Sales, Business Development 1232\n", "Engineering, Information Technology 1071\n", "Health Care Provider 1053\n", "Information Technology 1045\n", " 945\n", "Other 780\n", "Accounting/Auditing, Finance 564\n", "Legal 447\n", "Finance, Sales 437\n", "Name: count, dtype: int64\n", "\n", "Empty string: 945\n", "'Not specified': 0\n", "NaN: 0\n", "\n", "๐ŸŽฏ TRULY EMPTY: 945\n" ] } ], "source": [ "# Check what's in required_skills\n", "print(\"๐Ÿ” REQUIRED_SKILLS CHECK\")\n", "print(\"=\" * 80)\n", "\n", "print(f\"\\nTotal companies: {len(companies_full):,}\")\n", "print(f\"\\nValue counts:\")\n", "print(companies_full['required_skills'].value_counts().head(10))\n", "\n", "print(f\"\\nEmpty string: {(companies_full['required_skills'] == '').sum()}\")\n", "print(f\"'Not specified': {(companies_full['required_skills'] == 'Not specified').sum()}\")\n", "print(f\"NaN: {companies_full['required_skills'].isna().sum()}\")\n", "\n", "# Real check\n", "truly_empty = (companies_full['required_skills'] == '') | \\\n", " (companies_full['required_skills'] == 'Not specified') | \\\n", " (companies_full['required_skills'].isna())\n", "\n", "print(f\"\\n๐ŸŽฏ TRULY EMPTY: {truly_empty.sum():,}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 18: Export Results to CSV" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ’พ Exporting 50 candidates (top 5 each)...\n", "\n", " Processing 1/50...\n", "\n", "โœ… Exported 250 matches\n", "๐Ÿ“„ File: ../results/hrhub_matches.csv\n", "\n" ] } ], "source": [ "# ============================================================================\n", "# ๐Ÿ’พ EXPORT MATCHES TO CSV\n", "# ============================================================================\n", "\n", "def export_matches_to_csv(num_candidates=100, top_k=10):\n", " print(f\"๐Ÿ’พ Exporting {num_candidates} candidates (top {top_k} each)...\\n\")\n", " \n", " results = []\n", " \n", " for i in range(min(num_candidates, len(candidates))):\n", " if i % 50 == 0:\n", " print(f\" Processing {i+1}/{num_candidates}...\")\n", " \n", " matches = find_top_matches(i, top_k=top_k)\n", " cand = candidates.iloc[i]\n", " \n", " for rank, (comp_idx, score) in enumerate(matches, 1):\n", " if comp_idx >= len(companies_full):\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " \n", " results.append({\n", " 'candidate_id': i,\n", " 'candidate_category': cand.get('Category', 'N/A'),\n", " 'company_id': company.get('company_id', 'N/A'),\n", " 'company_name': company.get('name', 'N/A'),\n", " 'match_rank': rank,\n", " 'similarity_score': round(float(score), 4)\n", " })\n", " \n", " results_df = pd.DataFrame(results)\n", " output_file = f'{Config.RESULTS_PATH}hrhub_matches.csv'\n", " results_df.to_csv(output_file, index=False)\n", " \n", " print(f\"\\nโœ… Exported {len(results_df):,} matches\")\n", " print(f\"๐Ÿ“„ File: {output_file}\\n\")\n", " \n", " return results_df\n", "\n", "# Export sample\n", "matches_df = export_matches_to_csv(num_candidates=50, top_k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Interactive Visualization 1: t-SNE Vector Space\n", "\n", "Project embeddings from โ„ยณโธโด โ†’ โ„ยฒ to visualize candidates and companies" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐ŸŽจ VECTOR SPACE VISUALIZATION\n", "\n", "======================================================================\n", "๐Ÿ“Š Visualizing:\n", " โ€ข 500 candidates\n", " โ€ข 2000 companies\n", " โ€ข From โ„^384 โ†’ โ„ยฒ (t-SNE)\n", "\n", "๐Ÿ”„ Running t-SNE (2-3 minutes)...\n", "\n", "โœ… t-SNE complete!\n" ] } ], "source": [ "# ============================================================================\n", "# ๐ŸŽจ T-SNE VECTOR SPACE VISUALIZATION\n", "# ============================================================================\n", "\n", "from sklearn.manifold import TSNE\n", "\n", "print(\"๐ŸŽจ VECTOR SPACE VISUALIZATION\\n\")\n", "print(\"=\" * 70)\n", "\n", "# Sample for visualization\n", "n_cand_viz = min(500, len(candidates))\n", "n_comp_viz = min(2000, len(companies_full))\n", "\n", "print(f\"๐Ÿ“Š Visualizing:\")\n", "print(f\" โ€ข {n_cand_viz} candidates\")\n", "print(f\" โ€ข {n_comp_viz} companies\")\n", "print(f\" โ€ข From โ„^384 โ†’ โ„ยฒ (t-SNE)\\n\")\n", "\n", "# Sample vectors\n", "cand_sample = cand_vectors[:n_cand_viz]\n", "comp_sample = comp_vectors[:n_comp_viz]\n", "all_vectors = np.vstack([cand_sample, comp_sample])\n", "\n", "print(\"๐Ÿ”„ Running t-SNE (2-3 minutes)...\")\n", "tsne = TSNE(\n", " n_components=2,\n", " perplexity=30,\n", " random_state=42,\n", " n_iter=1000\n", ")\n", "\n", "vectors_2d = tsne.fit_transform(all_vectors)\n", "cand_2d = vectors_2d[:n_cand_viz]\n", "comp_2d = vectors_2d[n_cand_viz:]\n", "\n", "print(\"\\nโœ… t-SNE complete!\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{text}", "marker": { "color": "#ff6b6b", "opacity": 0.6, "size": 6 }, "mode": "markers", "name": "Companies", "text": [ "Company: IBM", "Company: GE HealthCare", "Company: Hewlett Packard Enterprise", "Company: Oracle", "Company: Accenture", "Company: Microsoft", "Company: Deloitte", "Company: Siemens", "Company: PwC", "Company: AT&T", "Company: Intel Corporation", "Company: Ericsson", "Company: Cisco", "Company: Motorola Mobility (a Lenovo Co", "Company: JPMorgan Chase & Co.", "Company: Nokia", "Company: EY", "Company: KPMG US", "Company: NXP Semiconductors", "Company: Philips", "Company: Verizon", "Company: SAP", "Company: Procter & Gamble", "Company: Bank of America", "Company: Elite Technology", "Company: BT Group", "Company: Pfizer", "Company: Johnson & Johnson", "Company: UBS", "Company: US Army Corps of Engineers", "Company: Wells Fargo", "Company: Unilever", "Company: Sony", "Company: Sony Electronics", "Company: Sony Pictures Entertainment", "Company: Atos", "Company: Deutsche Bank", "Company: DWS Group", "Company: Chubb", "Company: Shell", "Company: American Express", "Company: Unisys", "Company: Infosys", "Company: Yahoo", "Company: The Walt Disney Company", "Company: Fidelity Investments", "Company: Wipro", "Company: LinkedIn", "Company: Air Force Research Laboratory", "Company: Honeywell", "Company: Tata Consultancy Services", "Company: National Security Agency", "Company: National Computer Systems", "Company: McKinsey & Company", "Company: Xerox", "Company: Fujitsu Network Communications", "Company: Goldman Sachs", "Company: Boeing", "Company: bp", "Company: T-Mobile", "Company: Nestlรฉ", "Company: GSK", "Company: Thomson Reuters", "Company: Booz Allen Hamilton", "Company: Novartis", "Company: Northrop Grumman", "Company: CGI", "Company: Capital One", "Company: Barclays", "Company: PepsiCo", "Company: Google", "Company: Electronic Arts (EA)", "Company: SUSE", "Company: ADP", "Company: CDK Global", "Company: Teradata", "Company: SLB", "Company: General Motors", "Company: Ally", "Company: Adobe", "Company: eBay", "Company: PayPal", "Company: Ford Motor Company", "Company: Merck", "Company: SAS", "Company: Avaya", "Company: AMD", "Company: MIT Lincoln Laboratory", "Company: Raytheon", "Company: BNP Paribas", "Company: Mondelฤ“z International", "Company: Eastman Kodak Company", "Company: Carestream", "Company: UPS", "Company: Agilent Technologies", "Company: The Home Depot", "Company: Amdocs", "Company: Mars", "Company: Kaiser Permanente", "Company: Amazon", "Company: BMC Software", "Company: Roche", "Company: AstraZeneca", "Company: Abbott", "Company: SAIC", "Company: Dignity Health", "Company: Owens & Minor", "Company: Stanford Children's Health | L", "Company: Boston Scientific", "Company: Sanofi", "Company: Harvard Medical School", "Company: Harvard University", "Company: Harvard Law School", "Company: Dana-Farber Cancer Institute", "Company: Boston Children's Hospital", "Company: Beth Israel Deaconess Medical ", "Company: L'Orรฉal", "Company: Eli Lilly and Company", "Company: Intuit", "Company: FedEx Ground", "Company: FedEx Services", "Company: Ogilvy", "Company: Gap Inc.", "Company: Banana Republic", "Company: Cognizant", "Company: Robert Half", "Company: ExxonMobil", "Company: Societe Generale", "Company: The Coca-Cola Company", "Company: Comcast", "Company: Nielsen", "Company: HCLTech", "Company: AIG", "Company: BBC", "Company: State Street", "Company: Bristol Myers Squibb", "Company: Boston Consulting Group (BCG)", "Company: SLAC National Accelerator Labo", "Company: Stanford University School of ", "Company: Stanford University", "Company: ManpowerGroup", "Company: RBC", "Company: TotalEnergies", "Company: NBC News", "Company: NBCUniversal", "Company: CNBC", "Company: Allstate", "Company: Medtronic", "Company: Prudential Financial", "Company: Charles Schwab", "Company: 3M", "Company: Capco Energy Solutions", "Company: Marsh", "Company: Autodesk", "Company: BAE Systems, Inc.", "Company: Nickelodeon", "Company: Bayer", "Company: McKesson", "Company: General Dynamics Information T", "Company: General Dynamics Land Systems", "Company: General Dynamics Mission Syste", "Company: Philip Morris International", "Company: McCann Worldgroup", "Company: MRM", "Company: UM Worldwide", "Company: The Adecco Group", "Company: PTC", "Company: Thales", "Company: Sogeti", "Company: Rabobank", "Company: Mavenir", "Company: NASA - National Aeronautics an", "Company: Qualcomm", "Company: Applied Materials", "Company: Western Union", "Company: Nike", "Company: Spectrum Enterprise", "Company: Coldwell Banker Realty", "Company: Aon", "Company: CNN", "Company: TE Connectivity", "Company: Amgen", "Company: Gartner", "Company: Volvo Group", "Company: Volvo Penta", "Company: Volvo Buses", "Company: Mack Trucks", "Company: Volvo Construction Equipment", "Company: NetApp", "Company: Toyota North America", "Company: Bain & Company", "Company: Avis Budget Group", "Company: Best Buy", "Company: Pearson", "Company: Infineon Technologies", "Company: TEKsystems", "Company: Allegis Group", "Company: DuPont", "Company: Cadence Design Systems", "Company: Cardinal Health", "Company: Department for Transport (DfT)", "Company: Visa", "Company: Chevron", "Company: Canon Solutions America", "Company: Bosch Security and Safety Syst", "Company: LexisNexis", "Company: MetLife", "Company: Halliburton", "Company: KBR, Inc.", "Company: Keller Williams Realty, Inc.", "Company: Novo Nordisk", "Company: Hanesbrands Inc.", "Company: Danone", "Company: Juniper Networks", "Company: Johnson Controls", "Company: Victoriaโ€™s Secret & Co.", "Company: Bath & Body Works", "Company: Spherion", "Company: Starbucks", "Company: Delta Air Lines", "Company: Genentech", "Company: Flex", "Company: The Wall Street Journal", "Company: Dow Jones", "Company: Macy's", "Company: Insight", "Company: Kelly", "Company: Marriott International", "Company: CBRE", "Company: Randstad", "Company: Schneider Electric", "Company: Nationwide", "Company: Baxter International Inc.", "Company: United Airlines", "Company: State Farm", "Company: Dun & Bradstreet", "Company: Mercer", "Company: Pratt & Whitney", "Company: Carrier HVAC", "Company: Grant Thornton LLP (US)", "Company: Alstom", "Company: Northwestern Mutual", "Company: Hilton", "Company: Oliver Wyman", "Company: Synopsys Inc", "Company: Zurich North America", "Company: Digitas North America", "Company: The Hartford", "Company: UCLA Health", "Company: Children's Hospital Los Angele", "Company: UCLA", "Company: Wolters Kluwer", "Company: Cigna Healthcare", "Company: Bloomberg", "Company: Diageo", "Company: Rockwell Automation", "Company: Michigan Medicine", "Company: University of Michigan", "Company: U.S. Bank", "Company: Experian", "Company: iHeartMedia", "Company: Clear Channel Outdoor", "Company: Whirlpool Corporation", "Company: Dow", "Company: Ingram Micro", "Company: Crรฉdit Agricole CIB", "Company: University of Washington", "Company: Momentum Worldwide", "Company: Eaton", "Company: Tetra Pak", "Company: Panasonic Automotive North Ame", "Company: Panasonic North America", "Company: Panasonic Avionics Corporation", "Company: Caterpillar Inc.", "Company: Columbia University Irving Med", "Company: Columbia University", "Company: BASF", "Company: American Airlines", "Company: Citrix", "Company: Walmart", "Company: University of Illinois Chicago", "Company: University of Illinois Urbana-", "Company: Caltrans", "Company: County of San Diego", "Company: CalPERS", "Company: California Department of Justi", "Company: Valeo", "Company: McDonald's", "Company: Cargill", "Company: John Hancock", "Company: Manulife", "Company: Liberty Mutual Insurance", "Company: OpenText", "Company: KLA", "Company: BOMBARDIER", "Company: RR Donnelley", "Company: Acxiom", "Company: IKEA", "Company: Colgate-Palmolive", "Company: Expedia Group", "Company: Emerson", "Company: TD", "Company: Andersen Corporation", "Company: Federal Reserve Board", "Company: Federal Reserve Bank of San Fr", "Company: Federal Reserve Bank of Boston", "Company: Sage", "Company: Publicis", "Company: General Mills", "Company: BlackBerry", "Company: Mary Kay Global", "Company: University of California, Sant", "Company: University of California, Davi", "Company: UC Davis Health", "Company: Commonwealth Bank", "Company: BDO USA", "Company: Visteon Corporation", "Company: Seagate Technology", "Company: Canon Business Process Service", "Company: ITT Inc.", "Company: Aerotek", "Company: Brigham and Women's Hospital", "Company: Massachusetts General Hospital", "Company: Newton-Wellesley Hospital", "Company: NYC Department of Education", "Company: Albertsons Companies", "Company: Shaw's Supermarkets", "Company: Acme Markets", "Company: The Save Mart Companies", "Company: Teradyne", "Company: S&P Global", "Company: Teacher Retirement System of T", "Company: Texas Health and Human Service", "Company: Texas Workforce Commission", "Company: Texas Attorney General", "Company: Allianz Life", "Company: Lexmark", "Company: Saint-Gobain", "Company: CSAA Insurance Group, a AAA In", "Company: CertainTeed", "Company: VMware", "Company: Transportation Security Admini", "Company: FEMA", "Company: U.S. Customs and Border Protec", "Company: Universal Music Group", "Company: Fifth Third Bank", "Company: Mastercard", "Company: Staples", "Company: Elsevier", "Company: University of California, San ", "Company: UCSF Health", "Company: Ameriprise Financial Services,", "Company: Sony Music Entertainment", "Company: Alcoa", "Company: University of Phoenix", "Company: Accor", "Company: Tech Mahindra", "Company: Broadcom", "Company: Kforce Inc", "Company: Thermo Fisher Scientific", "Company: University of Southern Califor", "Company: Travelers", "Company: Check Point Software Technolog", "Company: Reckitt", "Company: U.S. Department of State", "Company: BD", "Company: Office Depot", "Company: Lionbridge", "Company: Edwards Vacuum", "Company: FIS", "Company: The HEINEKEN Company", "Company: Hyatt Regency", "Company: Levi Strauss & Co.", "Company: Scotiabank", "Company: Freddie Mac", "Company: Stop & Shop", "Company: Software Engineering Institute", "Company: NYU Stern School of Business", "Company: The 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"Company: UC San Diego", "Company: UC San Diego Health", "Company: IDC", "Company: Celestica", "Company: FICO", "Company: Sodexo", "Company: Pizza Hut", "Company: Taco Bell", "Company: Yum! 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Hansen", "Company: FM Global", "Company: Fresenius Medical Care North A", "Company: Union Pacific Railroad", "Company: W. L. Gore & Associates", "Company: University of Kentucky", "Company: Cooley LLP", "Company: Michaels Stores", "Company: Yoh, A Day & Zimmermann Compan", "Company: Mayer Brown", "Company: Choice Hotels International", "Company: Rensselaer Polytechnic Institu", "Company: Advantage Technical", "Company: CBIZ", "Company: Lands'โ€‹ End", "Company: AppleOne Employment Services", "Company: UNSW", "Company: NYU Langone Health", "Company: Atlanticus", "Company: NetSuite", "Company: Agilysys", "Company: County of Santa Clara", "Company: Icahn School of Medicine at Mo", "Company: Match", "Company: 7-Eleven", "Company: Zions Bancorporation", "Company: Schindler Group", "Company: Schindler Elevator Corporation", "Company: Brunswick Corporation", "Company: ePlus inc.", "Company: Brady Corporation", "Company: FUJIFILM Holdings America Corp", "Company: Fresche Solutions", "Company: AMS", "Company: Thrivent", "Company: Schwan's Company", "Company: Baylor Scott & White Health", "Company: The Herbert Wertheim UF Scripp", "Company: CSX", "Company: Crain Communications", "Company: Oklahoma State University", "Company: Infogain", "Company: QuinStreet", "Company: Landis+Gyr", "Company: Safety-Kleen", "Company: Paul Hastings", "Company: Reed Smith LLP", "Company: Sutherland", "Company: Marcus & Millichap", "Company: Georgia State University", "Company: National Institute of Standard", "Company: NewYork-Presbyterian Hospital", "Company: Drรคger", "Company: Tata Technologies", "Company: Russell Investments", "Company: MSCI Inc.", "Company: Axis Communications", "Company: Federal Bureau of Investigatio", "Company: Oregon Department of Transport", "Company: Altria", "Company: HARMAN International", "Company: Kofax", "Company: Zimmer Biomet", "Company: ERM", "Company: Fortinet", "Company: Loyola Medicine", "Company: Loyola University Chicago", "Company: Microchip Technology Inc.", "Company: Greyhound Lines, Inc.", "Company: DaVita Kidney Care", "Company: Tetra Tech", "Company: 24 Hour Fitness", "Company: UT Southwestern Medical Center", "Company: Airlines Reporting Corporation", "Company: Select Medical", "Company: Boston Globe Media", "Company: Ansys", "Company: Connexity, Inc. ", "Company: Gallagher", "Company: SoftServe", "Company: Sappi", "Company: UMB Bank", "Company: Advocate Health Care", "Company: Shure Incorporated", "Company: Gucci", "Company: MSA - The Safety Company", "Company: Norfolk Southern", "Company: Live Nation Entertainment", "Company: dunnhumby", "Company: AMERICAN SYSTEMS", "Company: HOK", "Company: Gibson Dunn ", "Company: Marathon Oil Corporation", "Company: Pro Staff", "Company: Eisai US", "Company: Worley", "Company: Hollister Incorporated", "Company: Rockstar Games", "Company: ClubCorp", "Company: CAS", "Company: Enbridge", "Company: Pall Corporation", "Company: MSNBC", "Company: J.Crew", "Company: WVU Medicine", "Company: Philadelphia Housing Authority", "Company: Austin Independent School Dist", "Company: Incyte", "Company: Premier Inc.", "Company: B. Braun Medical Inc. (US)", "Company: MultiPlan", "Company: Sirva", "Company: Tulane University", "Company: SEPHORA", "Company: Southern Glazer's Wine & Spiri", "Company: Bitdefender", "Company: Milliken & Company", "Company: State of Indiana", "Company: Highmark Inc.", "Company: AEG", "Company: Devon Energy", "Company: Dice", "Company: Simpson Thacher & Bartlett LLP", "Company: Marriott Vacations Worldwide", "Company: Vertex Pharmaceuticals", "Company: Align Technology", "Company: Hennepin County", "Company: American Residential Services", "Company: Delta Dental Ins.", "Company: American Institutes for Resear", "Company: BJC HealthCare", "Company: Florida International Universi", "Company: RITE AID", "Company: The Depository Trust & Clearin", "Company: Strategic Staffing Solutions", "Company: Akin Gump Strauss Hauer & Feld", "Company: Holland & Knight LLP", "Company: Hibu", "Company: InterSystems", "Company: Benchmark", "Company: Jack Henry", "Company: Federal Deposit Insurance Corp", "Company: Carnival Corporation", "Company: Intertek", "Company: Baird", "Company: Indotronix International Corpo", "Company: International SOS", "Company: Columbia Sportswear Company", "Company: CoStar Group", "Company: Ross Stores, Inc.", "Company: IEEE", "Company: Response Companies", "Company: Rohde & Schwarz", "Company: Mercy", "Company: State of Tennessee", "Company: Ruder Finn", "Company: Meijer", "Company: American Medical Association", "Company: Auburn University", "Company: RS", "Company: SGK", "Company: Tag", "Company: Spectrum Brands, Inc", "Company: Nature Portfolio", "Company: NRG Energy", "Company: American Bar Association", "Company: NYS Department of Transportati", "Company: The University of Texas at San", "Company: Fred Hutch", "Company: PDS Tech Commercial, Inc.", "Company: Plastic Omnium", "Company: Gates Corporation", "Company: Acuity Brands", "Company: CEI", "Company: Bekaert", "Company: Norton Rose Fulbright", "Company: Jostens", "Company: CHS Inc.", "Company: Publix Super Markets", "Company: The Johns Hopkins University A", "Company: Mott MacDonald", "Company: University of New Hampshire", "Company: Ultimate Staffing", "Company: Brown-Forman", "Company: Planview", "Company: Sonoco", "Company: Academy of Art University", "Company: Sunrise Senior Living", "Company: Essendant", "Company: Mizuho", "Company: TC Transcontinental", "Company: Fordham University", "Company: Linedata", "Company: Orica", "Company: Huxley", "Company: Blue Cross Blue Shield of Mich", "Company: Comscore, Inc.", "Company: Domino's", "Company: The Leukemia & Lymphoma Societ", "Company: Crane Aerospace & Electronics", "Company: The Carlyle Group", "Company: CSL", "Company: TEAM LEWIS", "Company: Xcel Energy", "Company: TC Energy", "Company: AutoZone", "Company: Boston Medical Center (BMC)", "Company: NETGEAR", "Company: Woodward, Inc.", "Company: UHY LLP, Certified Public Acco", "Company: Brown & Brown Insurance", "Company: Vishay Intertechnology, Inc.", "Company: Priceline", "Company: Davis Polk & Wardwell LLP", "Company: Dillard's Inc.", "Company: Lonza", "Company: FirstEnergy", "Company: GM Financial", "Company: Oakley", "Company: D.R. Horton", "Company: RUSH University Medical Center", "Company: Barry Callebaut Group", "Company: Bulgari", "Company: Cedars-Sinai", "Company: Illumina", "Company: Inova Health", "Company: Maryland State Highway Adminis", "Company: Horizon Blue Cross Blue Shield", "Company: Lockton", "Company: Nexans", "Company: ECCO", "Company: Itron, Inc.", "Company: Newsweek", "Company: Sam's Club", "Company: Corestaff Services", "Company: McDermott International, Ltd", "Company: Lennox", "Company: Aurora Health Care", "Company: Daiichi Sankyo US", "Company: St. Jude Children's Research H", "Company: State of North Carolina", "Company: The Timken Company", "Company: University of Louisville", "Company: Johnson Matthey", "Company: Vistage Worldwide, Inc.", "Company: Cirque du Soleil Entertainment", "Company: Habitat for Humanity Internati", "Company: SS&C Technologies", "Company: Zones, LLC", "Company: Scientific Research Corporatio", "Company: University of California, Rive", "Company: National General", "Company: Emmis Corporation", "Company: GEODIS", "Company: Presidio", "Company: University of Arkansas", "Company: EBSCO Information Services", "Company: NVR, Inc.", "Company: AlixPartners", "Company: DICK'S Sporting Goods", "Company: Petco", "Company: Riverbed Technology", "Company: Nelson Connects", "Company: Space Dynamics Laboratory", "Company: Stevens Institute of Technolog", "Company: Blackstone", "Company: University of Maryland Baltimo", "Company: OUTFRONT Media", "Company: STERIS", "Company: Model N", "Company: TRC Companies, Inc.", "Company: BorgWarner", "Company: Proskauer Rose LLP", "Company: International Rescue Committee", "Company: Land O'Lakes, Inc.", "Company: Merkle", "Company: Texas Health Resources", "Company: The Children's Place", "Company: Popular Bank", "Company: IDEXX", "Company: PIMCO", "Company: Sword Group", "Company: Entrust", "Company: Exelixis", "Company: GHX", "Company: The Lubrizol Corporation", "Company: Milliman", "Company: State of Missouri", "Company: DAT Freight & Analytics", "Company: Mount Sinai Health System", "Company: Life Time Inc.", "Company: Culver Careers (CulverCareers.", "Company: GES - Global Experience Specia", "Company: Guy Carpenter", "Company: Mintz", "Company: AMETEK", "Company: Littler", "Company: Subway", "Company: Acosta", "Company: American Tower", "Company: Bentley University", "Company: Church & Dwight Co., Inc.", "Company: Deutsche Bahn", "Company: The Judge Group", "Company: Unit4", "Company: Huber Engineered Materials", "Company: Globant", "Company: Orkin", "Company: Master Electronics", "Company: Staffmark", "Company: Cartus", "Company: Quad", "Company: James Hardie", "Company: tms", "Company: Transocean", "Company: Dollar General", "Company: Callaway Golf", "Company: Equinix", "Company: Pactiv Evergreen Inc.", "Company: Procom", "Company: Fish & Richardson P.C.", "Company: New Balance", "Company: O-I", "Company: QIAGEN", "Company: Urban Outfitters", "Company: Anthropologie", "Company: Leonardo DRS", "Company: Talbots", "Company: ATR International", "Company: Banner Health", "Company: Charles River Laboratories", "Company: Husky Technologies", "Company: Altair", "Company: Sumitomo Mitsui Banking Corpor", "Company: University of Alaska Fairbanks", "Company: Alston & Bird", "Company: Munich Re", "Company: Dyson", "Company: The Guitar Center Company", "Company: MoneyGram International", "Company: Douglas Elliman Real Estate", "Company: Teleflex", "Company: Levi, Ray & Shoup, Inc. 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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Vector Space: Candidates & Companies (Enriched with Postings)" }, "width": 1200, "xaxis": { "title": { "text": "Dimension 1" } }, "yaxis": { "title": { "text": "Dimension 2" } } } } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "โœ… Visualization complete!\n", "๐Ÿ’ก If green & red OVERLAP โ†’ Alignment worked!\n" ] } ], "source": [ "# Create interactive plot\n", "fig = go.Figure()\n", "\n", "# Companies (red)\n", "fig.add_trace(go.Scatter(\n", " x=comp_2d[:, 0],\n", " y=comp_2d[:, 1],\n", " mode='markers',\n", " name='Companies',\n", " marker=dict(size=6, color='#ff6b6b', opacity=0.6),\n", " text=[f\"Company: {companies_full.iloc[i].get('name', 'N/A')[:30]}\" \n", " for i in range(n_comp_viz)],\n", " hovertemplate='%{text}'\n", "))\n", "\n", "# Candidates (green)\n", "fig.add_trace(go.Scatter(\n", " x=cand_2d[:, 0],\n", " y=cand_2d[:, 1],\n", " mode='markers',\n", " name='Candidates',\n", " marker=dict(\n", " size=10,\n", " color='#00ff00',\n", " opacity=0.8,\n", " line=dict(width=1, color='white')\n", " ),\n", " text=[f\"Candidate {i}\" for i in range(n_cand_viz)],\n", " hovertemplate='%{text}'\n", "))\n", "\n", "fig.update_layout(\n", " title='Vector Space: Candidates & Companies (Enriched with Postings)',\n", " xaxis_title='Dimension 1',\n", " yaxis_title='Dimension 2',\n", " width=1200,\n", " height=800,\n", " plot_bgcolor='#1a1a1a',\n", " paper_bgcolor='#0d0d0d',\n", " font=dict(color='white')\n", ")\n", "\n", "fig.show()\n", "\n", "print(\"\\nโœ… Visualization complete!\")\n", "print(\"๐Ÿ’ก If green & red OVERLAP โ†’ Alignment worked!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Interactive Visualization 2: Highlighted Match Network\n", "\n", "Show candidate and their top matches with connection lines" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ” Analyzing Candidate #0...\n", "\n" ] }, { "data": { 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created!\n", " โญ Green star = Candidate #0\n", " ๐Ÿ”ด Red dots = Top matches\n", " ๐Ÿ’› Yellow lines = Connections\n" ] } ], "source": [ "# ============================================================================\n", "# ๐Ÿ” HIGHLIGHTED MATCH NETWORK\n", "# ============================================================================\n", "\n", "target_candidate = 0\n", "\n", "print(f\"๐Ÿ” Analyzing Candidate #{target_candidate}...\\n\")\n", "\n", "matches = find_top_matches(target_candidate, top_k=10)\n", "match_indices = [comp_idx for comp_idx, score in matches if comp_idx < n_comp_viz]\n", "\n", "# Create highlighted plot\n", "fig2 = go.Figure()\n", "\n", "# All companies (background)\n", "fig2.add_trace(go.Scatter(\n", " x=comp_2d[:, 0],\n", " y=comp_2d[:, 1],\n", " mode='markers',\n", " name='All Companies',\n", " marker=dict(size=4, color='#ff6b6b', opacity=0.3),\n", " showlegend=True\n", "))\n", "\n", "# Top matches (highlighted)\n", "if match_indices:\n", " match_positions = comp_2d[match_indices]\n", " fig2.add_trace(go.Scatter(\n", " x=match_positions[:, 0],\n", " y=match_positions[:, 1],\n", " mode='markers',\n", " name='Top Matches',\n", " marker=dict(\n", " size=15,\n", " color='#ff0000',\n", " line=dict(width=2, color='white')\n", " ),\n", " text=[f\"Match #{i+1}: {companies_full.iloc[match_indices[i]].get('name', 'N/A')[:30]}
Score: {matches[i][1]:.3f}\" \n", " for i in range(len(match_indices))],\n", " hovertemplate='%{text}'\n", " ))\n", "\n", "# Target candidate (star)\n", "fig2.add_trace(go.Scatter(\n", " x=[cand_2d[target_candidate, 0]],\n", " y=[cand_2d[target_candidate, 1]],\n", " mode='markers',\n", " name=f'Candidate #{target_candidate}',\n", " marker=dict(\n", " size=25,\n", " color='#00ff00',\n", " symbol='star',\n", " line=dict(width=3, color='white')\n", " )\n", "))\n", "\n", "# Connection lines (top 5)\n", "for i, match_idx in enumerate(match_indices[:5]):\n", " fig2.add_trace(go.Scatter(\n", " x=[cand_2d[target_candidate, 0], comp_2d[match_idx, 0]],\n", " y=[cand_2d[target_candidate, 1], comp_2d[match_idx, 1]],\n", " mode='lines',\n", " line=dict(color='yellow', width=1, dash='dot'),\n", " opacity=0.5,\n", " showlegend=False\n", " ))\n", "\n", "fig2.update_layout(\n", " title=f'Candidate #{target_candidate} and Top Matches',\n", " xaxis_title='Dimension 1',\n", " yaxis_title='Dimension 2',\n", " width=1200,\n", " height=800,\n", " plot_bgcolor='#1a1a1a',\n", " paper_bgcolor='#0d0d0d',\n", " font=dict(color='white')\n", ")\n", "\n", "fig2.show()\n", "\n", "print(\"\\nโœ… Highlighted visualization created!\")\n", "print(f\" โญ Green star = Candidate #{target_candidate}\")\n", "print(f\" ๐Ÿ”ด Red dots = Top matches\")\n", "print(f\" ๐Ÿ’› Yellow lines = Connections\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐ŸŒ Interactive Visualization 3: Network Graph (PyVis)\n", "\n", "Interactive network showing candidate-company connections with nodes & edges" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐ŸŒ Creating interactive network graph...\n", "\n", "โœ… Network graph created!\n", "๐Ÿ“„ Saved: ../results/network_graph.html\n", "\n", "๐Ÿ’ก LEGEND:\n", " โญ Green star = Candidate #0\n", " ๐Ÿ”ด Red nodes = Companies (size = match score)\n", " ๐Ÿ’› Yellow edges = Connections\n", "\n", "โ„น๏ธ Hover over nodes to see details\n", " Drag nodes to rearrange\n", " Zoom with mouse wheel\n", "\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ============================================================================\n", "# ๐ŸŒ NETWORK GRAPH WITH PYVIS\n", "# ============================================================================\n", "\n", "from pyvis.network import Network\n", "import webbrowser\n", "import os\n", "\n", "print(\"๐ŸŒ Creating interactive network graph...\\n\")\n", "\n", "target_candidate = 0\n", "top_k_network = 10\n", "\n", "# Get matches\n", "matches = find_top_matches(target_candidate, top_k=top_k_network)\n", "\n", "# Create network\n", "net = Network(\n", " height='800px',\n", " width='100%',\n", " bgcolor='#1a1a1a',\n", " font_color='white',\n", " directed=False\n", ")\n", "\n", "# Configure physics\n", "net.barnes_hut(\n", " gravity=-5000,\n", " central_gravity=0.3,\n", " spring_length=100,\n", " spring_strength=0.01\n", ")\n", "\n", "# Add candidate node (center)\n", "cand = candidates.iloc[target_candidate]\n", "cand_label = f\"Candidate #{target_candidate}\"\n", "net.add_node(\n", " f'cand_{target_candidate}',\n", " label=cand_label,\n", " title=f\"{cand.get('Category', 'N/A')}
Skills: {str(cand.get('skills', 'N/A'))[:100]}\",\n", " color='#00ff00',\n", " size=40,\n", " shape='star'\n", ")\n", "\n", "# Add company nodes + edges\n", "for rank, (comp_idx, score) in enumerate(matches, 1):\n", " if comp_idx >= len(companies_full):\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " comp_name = company.get('name', f'Company {comp_idx}')[:30]\n", " \n", " # Color by score\n", " if score > 0.7:\n", " color = '#ff0000' # Red (strong match)\n", " elif score > 0.5:\n", " color = '#ff6b6b' # Light red (good match)\n", " else:\n", " color = '#ffaaaa' # Pink (weak match)\n", " \n", " # Add company node\n", " net.add_node(\n", " f'comp_{comp_idx}',\n", " label=f\"#{rank}. {comp_name}\",\n", " title=f\"Score: {score:.3f}
Industries: {str(company.get('industries_list', 'N/A'))[:50]}
Required: {str(company.get('required_skills', 'N/A'))[:100]}\",\n", " color=color,\n", " size=20 + (score * 20) # Size by score\n", " )\n", " \n", " # Add edge\n", " net.add_edge(\n", " f'cand_{target_candidate}',\n", " f'comp_{comp_idx}',\n", " value=float(score),\n", " title=f\"Similarity: {score:.3f}\",\n", " color='yellow'\n", " )\n", "\n", "# Save\n", "output_file = f'{Config.RESULTS_PATH}network_graph.html'\n", "net.save_graph(output_file)\n", "\n", "print(f\"โœ… Network graph created!\")\n", "print(f\"๐Ÿ“„ Saved: {output_file}\")\n", "print(f\"\\n๐Ÿ’ก LEGEND:\")\n", "print(f\" โญ Green star = Candidate #{target_candidate}\")\n", "print(f\" ๐Ÿ”ด Red nodes = Companies (size = match score)\")\n", "print(f\" ๐Ÿ’› Yellow edges = Connections\")\n", "print(f\"\\nโ„น๏ธ Hover over nodes to see details\")\n", "print(f\" Drag nodes to rearrange\")\n", "print(f\" Zoom with mouse wheel\\n\")\n", "\n", "# Display in notebook\n", "from IPython.display import IFrame\n", "IFrame(output_file, width=1000, height=800)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ๐Ÿ“Š Network Node Data\n", "\n", "Detailed information about nodes and connections" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ“Š NETWORK DATA SUMMARY\n", "================================================================================\n", "\n", "Total nodes: 11\n", " - 1 candidate node (green star)\n", " - 10 company nodes (red circles)\n", "\n", "Total edges: 10\n", "\n", "================================================================================\n", "\n", "๐ŸŽฏ CANDIDATE NODE:\n", " ID: cand_0\n", " Category: N/A\n", " Skills: ['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', ...\n", "\n", "๐Ÿข COMPANY NODES (Top 5):\n", "\n", " #1. Cloudera\n", " ID: comp_6537\n", " Score: 0.7106\n", " Industries: Software Development...\n", "\n", " #2. Info Services\n", " ID: comp_6383\n", " Score: 0.6445\n", " Industries: IT Services and IT Consulting...\n", "\n", " #3. CloudIngest\n", " ID: comp_20497\n", " Score: 0.6403\n", " Industries: Software Development...\n", "\n", " #4. Rackspace Technology\n", " ID: comp_739\n", " Score: 0.6319\n", " Industries: IT Services and IT Consulting...\n", "\n", " #5. DataStax\n", " ID: comp_10803\n", " Score: 0.6152\n", " Industries: IT Services and IT Consulting...\n", "\n", "================================================================================\n" ] } ], "source": [ "# ============================================================================\n", "# DISPLAY NODE DATA\n", "# ============================================================================\n", "\n", "print(\"๐Ÿ“Š NETWORK DATA SUMMARY\")\n", "print(\"=\" * 80)\n", "print(f\"\\nTotal nodes: {1 + len(matches)}\")\n", "print(f\" - 1 candidate node (green star)\")\n", "print(f\" - {len(matches)} company nodes (red circles)\")\n", "print(f\"\\nTotal edges: {len(matches)}\")\n", "print(f\"\\n\" + \"=\" * 80)\n", "\n", "# Show node details\n", "print(f\"\\n๐ŸŽฏ CANDIDATE NODE:\")\n", "print(f\" ID: cand_{target_candidate}\")\n", "print(f\" Category: {cand.get('Category', 'N/A')}\")\n", "print(f\" Skills: {str(cand.get('skills', 'N/A'))[:100]}...\")\n", "\n", "print(f\"\\n๐Ÿข COMPANY NODES (Top 5):\")\n", "for rank, (comp_idx, score) in enumerate(matches[:5], 1):\n", " if comp_idx < len(companies_full):\n", " company = companies_full.iloc[comp_idx]\n", " print(f\"\\n #{rank}. {company.get('name', 'N/A')[:40]}\")\n", " print(f\" ID: comp_{comp_idx}\")\n", " print(f\" Score: {score:.4f}\")\n", " print(f\" Industries: {str(company.get('industries_list', 'N/A'))[:60]}...\")\n", "\n", "print(f\"\\n\" + \"=\" * 80)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ” Visualization 4: Display Node Data\n", "\n", "Inspect detailed information about candidates and companies" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================================================================================\n", "๐ŸŸข CANDIDATE #0\n", "================================================================================\n", "\n", "๐Ÿ“Š KEY INFORMATION:\n", "\n", "Resume ID: N/A\n", "Category: N/A\n", "Skills: ['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++', 'Data Structures', 'DBMS', 'RDBMS', 'Informatica\n", "Career Objective: Big data analytics working and database warehouse manager with robust experience in handling all kinds of data. I have also used multiple cloud infrastructure services and am well acquainted with them\n", "\n", "================================================================================\n", "\n", "๐ŸŽฏ TOP 5 MATCHES:\n", "================================================================================\n", "#1. Cloudera (Score: 0.7106)\n", "#2. Info Services (Score: 0.6445)\n", "#3. CloudIngest (Score: 0.6403)\n", "#4. Rackspace Technology (Score: 0.6319)\n", "#5. DataStax (Score: 0.6152)\n", "\n", "================================================================================\n" ] } ], "source": [ "# ============================================================================\n", "# DISPLAY NODE DATA - See what's behind the graph\n", "# ============================================================================\n", "\n", "def display_node_data(node_id):\n", " print(\"=\" * 80)\n", " \n", " if node_id.startswith('C'):\n", " # CANDIDATE\n", " cand_idx = int(node_id[1:])\n", " \n", " if cand_idx >= len(candidates):\n", " print(f\"โŒ Candidate {cand_idx} not found!\")\n", " return\n", " \n", " candidate = candidates.iloc[cand_idx]\n", " \n", " print(f\"๐ŸŸข CANDIDATE #{cand_idx}\")\n", " print(\"=\" * 80)\n", " print(f\"\\n๐Ÿ“Š KEY INFORMATION:\\n\")\n", " print(f\"Resume ID: {candidate.get('Resume_ID', 'N/A')}\")\n", " print(f\"Category: {candidate.get('Category', 'N/A')}\")\n", " print(f\"Skills: {str(candidate.get('skills', 'N/A'))[:200]}\")\n", " print(f\"Career Objective: {str(candidate.get('career_objective', 'N/A'))[:200]}\")\n", " \n", " elif node_id.startswith('J'):\n", " # COMPANY\n", " comp_idx = int(node_id[1:])\n", " \n", " if comp_idx >= len(companies_full):\n", " print(f\"โŒ Company {comp_idx} not found!\")\n", " return\n", " \n", " company = companies_full.iloc[comp_idx]\n", " \n", " print(f\"๐Ÿ”ด COMPANY #{comp_idx}\")\n", " print(\"=\" * 80)\n", " print(f\"\\n๐Ÿ“Š COMPANY INFORMATION:\\n\")\n", " print(f\"Name: {company.get('name', 'N/A')}\")\n", " print(f\"Industries: {str(company.get('industries_list', 'N/A'))[:200]}\")\n", " print(f\"Required Skills: {str(company.get('required_skills', 'N/A'))[:200]}\")\n", " print(f\"Posted Jobs: {str(company.get('posted_job_titles', 'N/A'))[:200]}\")\n", " \n", " print(\"\\n\" + \"=\" * 80 + \"\\n\")\n", "\n", "def display_node_with_connections(node_id, top_k=10):\n", " display_node_data(node_id)\n", " \n", " if node_id.startswith('C'):\n", " cand_idx = int(node_id[1:])\n", " \n", " print(f\"๐ŸŽฏ TOP {top_k} MATCHES:\")\n", " print(\"=\" * 80)\n", " \n", " matches = find_top_matches(cand_idx, top_k=top_k)\n", " \n", " # FIXED: Validate indices before accessing\n", " valid_matches = 0\n", " for rank, (comp_idx, score) in enumerate(matches, 1):\n", " # Check if index is valid\n", " if comp_idx >= len(companies_full):\n", " print(f\"โš ๏ธ Match #{rank}: Index {comp_idx} out of range (skipping)\")\n", " continue\n", " \n", " company = companies_full.iloc[comp_idx]\n", " print(f\"#{rank}. {company.get('name', 'N/A')[:40]} (Score: {score:.4f})\")\n", " valid_matches += 1\n", " \n", " if valid_matches == 0:\n", " print(\"โš ๏ธ No valid matches found (all indices out of bounds)\")\n", " print(\"\\n๐Ÿ’ก SOLUTION: Regenerate embeddings after deduplication!\")\n", " \n", " print(\"\\n\" + \"=\" * 80)\n", "\n", "# Example usage\n", "display_node_with_connections('C0', top_k=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ•ธ๏ธ Visualization 5: NetworkX Graph\n", "\n", "Network graph using NetworkX + Plotly with force-directed layout" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ•ธ๏ธ Creating NETWORK GRAPH...\n", "\n", "๐Ÿ“Š Network size:\n", " โ€ข 20 candidates\n", " โ€ข 5 companies per candidate\n", "\n", "โœ… Network created!\n", " Nodes: 68\n", " Edges: 100\n", "\n", "๐Ÿ”„ Calculating layout...\n", "โœ… Layout done!\n", "\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hoverinfo": "none", "line": { "color": "rgba(255,255,255,0.3)", "width": 2.1317728757858276 }, "mode": "lines", "showlegend": false, "type": "scatter", "x": [ -0.12494252837111805, 0.7944199848313134, null ], "y": [ 0.9848184222534605, 0.49962778953892634, null ] }, { "hoverinfo": "none", "line": { "color": "rgba(255,255,255,0.3)", "width": 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"gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Network Graph: Candidates โ†” Companies" }, "width": 1400, "xaxis": { "showgrid": false, "showticklabels": false, "zeroline": false }, "yaxis": { "showgrid": false, "showticklabels": false, "zeroline": false } } } }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "โœ… NetworkX graph created!\n", " ๐ŸŸข Green = Candidates\n", " ๐Ÿ”ด Red = Companies\n", " Lines = Connections (thicker = stronger)\n", "\n" ] } ], "source": [ "# ============================================================================\n", "# NETWORK GRAPH WITH NETWORKX + PLOTLY\n", "# ============================================================================\n", "\n", "import networkx as nx\n", "\n", "print(\"๐Ÿ•ธ๏ธ Creating NETWORK GRAPH...\\n\")\n", "\n", "# Create graph\n", "G = nx.Graph()\n", "\n", "# Sample\n", "n_cand_sample = min(20, len(candidates))\n", "top_k_per_cand = 5\n", "\n", "print(f\"๐Ÿ“Š Network size:\")\n", "print(f\" โ€ข {n_cand_sample} candidates\")\n", "print(f\" โ€ข {top_k_per_cand} companies per candidate\\n\")\n", "\n", "# Add nodes + edges\n", "companies_in_graph = set()\n", "\n", "for i in range(n_cand_sample):\n", " G.add_node(f\"C{i}\", node_type='candidate', label=f\"C{i}\")\n", " \n", " matches = find_top_matches(i, top_k=top_k_per_cand)\n", " \n", " for comp_idx, score in matches:\n", " comp_id = f\"J{comp_idx}\"\n", " \n", " if comp_id not in companies_in_graph:\n", " company_name = companies_full.iloc[comp_idx].get('name', 'N/A')[:20]\n", " G.add_node(comp_id, node_type='company', label=company_name)\n", " companies_in_graph.add(comp_id)\n", " \n", " G.add_edge(f\"C{i}\", comp_id, weight=float(score))\n", "\n", "print(f\"โœ… Network created!\")\n", "print(f\" Nodes: {G.number_of_nodes()}\")\n", "print(f\" Edges: {G.number_of_edges()}\\n\")\n", "\n", "# Calculate layout\n", "print(\"๐Ÿ”„ Calculating layout...\")\n", "pos = nx.spring_layout(G, k=2, iterations=50, seed=42)\n", "print(\"โœ… Layout done!\\n\")\n", "\n", "# Create edge traces\n", "edge_trace = []\n", "for edge in G.edges(data=True):\n", " x0, y0 = pos[edge[0]]\n", " x1, y1 = pos[edge[1]]\n", " weight = edge[2]['weight']\n", " \n", " edge_trace.append(go.Scatter(\n", " x=[x0, x1, None],\n", " y=[y0, y1, None],\n", " mode='lines',\n", " line=dict(width=weight*3, color='rgba(255,255,255,0.3)'),\n", " hoverinfo='none',\n", " showlegend=False\n", " ))\n", "\n", "# Candidate nodes\n", "cand_nodes = [n for n, d in G.nodes(data=True) if d['node_type']=='candidate']\n", "cand_x = [pos[n][0] for n in cand_nodes]\n", "cand_y = [pos[n][1] for n in cand_nodes]\n", "cand_labels = [G.nodes[n]['label'] for n in cand_nodes]\n", "\n", "candidate_trace = go.Scatter(\n", " x=cand_x, y=cand_y,\n", " mode='markers+text',\n", " name='Candidates',\n", " marker=dict(size=25, color='#00ff00', line=dict(width=2, color='white')),\n", " text=cand_labels,\n", " textposition='top center',\n", " hovertemplate='%{text}'\n", ")\n", "\n", "# Company nodes\n", "comp_nodes = [n for n, d in G.nodes(data=True) if d['node_type']=='company']\n", "comp_x = [pos[n][0] for n in comp_nodes]\n", "comp_y = [pos[n][1] for n in comp_nodes]\n", "comp_labels = [G.nodes[n]['label'] for n in comp_nodes]\n", "\n", "company_trace = go.Scatter(\n", " x=comp_x, y=comp_y,\n", " mode='markers+text',\n", " name='Companies',\n", " marker=dict(size=15, color='#ff6b6b', symbol='square'),\n", " text=comp_labels,\n", " textposition='top center',\n", " hovertemplate='%{text}'\n", ")\n", "\n", "# Create figure\n", "fig = go.Figure(data=edge_trace + [candidate_trace, company_trace])\n", "\n", "fig.update_layout(\n", " title='Network Graph: Candidates โ†” Companies',\n", " showlegend=True,\n", " width=1400, height=900,\n", " plot_bgcolor='#1a1a1a',\n", " paper_bgcolor='#0d0d0d',\n", " font=dict(color='white'),\n", " xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n", " yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)\n", ")\n", "\n", "fig.show()\n", "\n", "print(\"โœ… NetworkX graph created!\")\n", "print(\" ๐ŸŸข Green = Candidates\")\n", "print(\" ๐Ÿ”ด Red = Companies\")\n", "print(\" Lines = Connections (thicker = stronger)\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ› DEBUG: Why aren't candidates & companies overlapping?\n", "\n", "Investigating the embedding space alignment" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ› DEBUGGING EMBEDDING SPACE\n", "================================================================================\n", "\n", "1๏ธโƒฃ VECTOR SHAPES:\n", " Candidates: (9544, 384)\n", " Companies: (24473, 384)\n", "\n", "2๏ธโƒฃ VECTOR NORMS (should be ~1.0 if normalized):\n", " Candidates: mean=1.0000, min=1.0000, max=1.0000\n", " Companies: mean=1.0000, min=1.0000, max=1.0000\n", "\n", "3๏ธโƒฃ SAMPLE SIMILARITIES:\n", " Candidate #0 top 5 matches:\n", " #1. Company 6537: 0.7106\n", " #2. Company 6383: 0.6445\n", " #3. Company 20497: 0.6403\n", " #4. Company 739: 0.6319\n", " #5. Company 10803: 0.6152\n", "\n", "4๏ธโƒฃ TEXT REPRESENTATION SAMPLES:\n", "\n", " ๐Ÿ“‹ CANDIDATE #0:\n", " Skills: ['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', \n", " Category: N/A\n", "\n", " ๐Ÿข TOP MATCH COMPANY #6537:\n", " Name: Cloudera\n", " Required Skills: Product Management, Marketing, Design, Art/Creative, Information Technology, Information Technology\n", " Industries: Software Development\n", "\n", "5๏ธโƒฃ POSTINGS ENRICHMENT CHECK:\n", " WITH postings: 23,528 (96.1%)\n", " WITHOUT postings: 945\n", "\n", "โ“ HYPOTHESIS:\n", " โœ… Most companies have postings\n", " โ“ Need to check if embeddings were generated AFTER enrichment\n", "\n", "================================================================================\n" ] } ], "source": [ "# ============================================================================\n", "# DEBUG: CHECK EMBEDDING ALIGNMENT\n", "# ============================================================================\n", "\n", "print(\"๐Ÿ› DEBUGGING EMBEDDING SPACE\")\n", "print(\"=\" * 80)\n", "\n", "# 1. Check if vectors loaded correctly\n", "print(f\"\\n1๏ธโƒฃ VECTOR SHAPES:\")\n", "print(f\" Candidates: {cand_vectors.shape}\")\n", "print(f\" Companies: {comp_vectors.shape}\")\n", "\n", "# 2. Check vector norms\n", "print(f\"\\n2๏ธโƒฃ VECTOR NORMS (should be ~1.0 if normalized):\")\n", "cand_norms = np.linalg.norm(cand_vectors, axis=1)\n", "comp_norms = np.linalg.norm(comp_vectors, axis=1)\n", "print(f\" Candidates: mean={cand_norms.mean():.4f}, min={cand_norms.min():.4f}, max={cand_norms.max():.4f}\")\n", "print(f\" Companies: mean={comp_norms.mean():.4f}, min={comp_norms.min():.4f}, max={comp_norms.max():.4f}\")\n", "\n", "# 3. Sample similarity\n", "print(f\"\\n3๏ธโƒฃ SAMPLE SIMILARITIES:\")\n", "sample_cand = 0\n", "matches = find_top_matches(sample_cand, top_k=5)\n", "print(f\" Candidate #{sample_cand} top 5 matches:\")\n", "for rank, (comp_idx, score) in enumerate(matches, 1):\n", " print(f\" #{rank}. Company {comp_idx}: {score:.4f}\")\n", "\n", "# 4. Check text representations\n", "print(f\"\\n4๏ธโƒฃ TEXT REPRESENTATION SAMPLES:\")\n", "print(f\"\\n ๐Ÿ“‹ CANDIDATE #{sample_cand}:\")\n", "cand = candidates.iloc[sample_cand]\n", "print(f\" Skills: {str(cand.get('skills', 'N/A'))[:100]}\")\n", "print(f\" Category: {cand.get('Category', 'N/A')}\")\n", "\n", "top_company_idx = matches[0][0]\n", "print(f\"\\n ๐Ÿข TOP MATCH COMPANY #{top_company_idx}:\")\n", "company = companies_full.iloc[top_company_idx]\n", "print(f\" Name: {company.get('name', 'N/A')}\")\n", "print(f\" Required Skills: {str(company.get('required_skills', 'N/A'))[:100]}\")\n", "print(f\" Industries: {str(company.get('industries_list', 'N/A'))[:100]}\")\n", "\n", "# 5. Check if postings enrichment worked\n", "print(f\"\\n5๏ธโƒฃ POSTINGS ENRICHMENT CHECK:\")\n", "companies_with_postings = companies_full[companies_full['required_skills'] != ''].shape[0]\n", "companies_without = companies_full[companies_full['required_skills'] == ''].shape[0]\n", "print(f\" WITH postings: {companies_with_postings:,} ({companies_with_postings/len(companies_full)*100:.1f}%)\")\n", "print(f\" WITHOUT postings: {companies_without:,}\")\n", "\n", "# 6. HYPOTHESIS\n", "print(f\"\\nโ“ HYPOTHESIS:\")\n", "if companies_without > companies_with_postings:\n", " print(f\" โš ๏ธ Most companies DON'T have postings!\")\n", " print(f\" โš ๏ธ They only have: industries, specialties, description\")\n", " print(f\" โš ๏ธ This creates DIFFERENT language than candidates\")\n", " print(f\"\\n ๐Ÿ’ก SOLUTION:\")\n", " print(f\" Option A: Filter to only companies WITH postings\")\n", " print(f\" Option B: Use LLM to translate industries โ†’ skills\")\n", "else:\n", " print(f\" โœ… Most companies have postings\")\n", " print(f\" โ“ Need to check if embeddings were generated AFTER enrichment\")\n", "\n", "print(f\"\\n\" + \"=\" * 80)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "## ๐Ÿ“Š Step 19: Summary\n", "\n", "### What We Built" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "======================================================================\n", "๐ŸŽฏ HRHUB v2.1 - SUMMARY\n", "======================================================================\n", "\n", "โœ… IMPLEMENTED:\n", " 1. Zero-Shot Job Classification (Entry/Mid/Senior/Executive)\n", " 2. Few-Shot Learning with Examples\n", " 3. Structured Skills Extraction (Pydantic schemas)\n", " 4. Match Explainability (LLM-generated reasoning)\n", " 5. FREE LLM Integration (Hugging Face)\n", " 6. Flexible Data Loading (Upload OR Google Drive)\n", "\n", "๐Ÿ’ฐ COST: $0.00 (completely free!)\n", "\n", "๐Ÿ“ˆ COURSE ALIGNMENT:\n", " โœ… LLMs for structured output\n", " โœ… Pydantic schemas\n", " โœ… Classification pipelines\n", " โœ… Zero-shot & few-shot learning\n", " โœ… JSON extraction\n", " โœ… Transformer architecture (embeddings)\n", " โœ… API deployment strategies\n", "\n", "======================================================================\n", "๐Ÿš€ READY TO MOVE TO VS CODE!\n", "======================================================================\n" ] } ], "source": [ "print(\"=\"*70)\n", "print(\"๐ŸŽฏ HRHUB v2.1 - SUMMARY\")\n", "print(\"=\"*70)\n", "print(\"\")\n", "print(\"โœ… IMPLEMENTED:\")\n", "print(\" 1. Zero-Shot Job Classification (Entry/Mid/Senior/Executive)\")\n", "print(\" 2. Few-Shot Learning with Examples\")\n", "print(\" 3. Structured Skills Extraction (Pydantic schemas)\")\n", "print(\" 4. Match Explainability (LLM-generated reasoning)\")\n", "print(\" 5. FREE LLM Integration (Hugging Face)\")\n", "print(\" 6. Flexible Data Loading (Upload OR Google Drive)\")\n", "print(\"\")\n", "print(\"๐Ÿ’ฐ COST: $0.00 (completely free!)\")\n", "print(\"\")\n", "print(\"๐Ÿ“ˆ COURSE ALIGNMENT:\")\n", "print(\" โœ… LLMs for structured output\")\n", "print(\" โœ… Pydantic schemas\")\n", "print(\" โœ… Classification pipelines\")\n", "print(\" โœ… Zero-shot & few-shot learning\")\n", "print(\" โœ… JSON extraction\")\n", "print(\" โœ… Transformer architecture (embeddings)\")\n", "print(\" โœ… API deployment strategies\")\n", "print(\"\")\n", "print(\"=\"*70)\n", "print(\"๐Ÿš€ READY TO MOVE TO VS CODE!\")\n", "print(\"=\"*70)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐ŸŒŒ GENERATING t-SNE VISUALIZATION...\n", "================================================================================\n", "\n", "๐Ÿ“Š Sampling:\n", " Candidates: 2,000\n", " Companies: 2,000\n", "\n", "๐Ÿ”„ Running t-SNE (this takes ~2-3 min)...\n", "[t-SNE] Computing 91 nearest neighbors...\n", "[t-SNE] Indexed 4000 samples in 0.001s...\n", "[t-SNE] Computed neighbors for 4000 samples in 0.457s...\n", "[t-SNE] Computed conditional probabilities for sample 1000 / 4000\n", "[t-SNE] Computed conditional probabilities for sample 2000 / 4000\n", "[t-SNE] Computed conditional probabilities for sample 3000 / 4000\n", "[t-SNE] Computed conditional probabilities for sample 4000 / 4000\n", "[t-SNE] Mean sigma: 0.279985\n", "[t-SNE] KL divergence after 250 iterations with early exaggeration: 75.840073\n", "[t-SNE] KL divergence after 1000 iterations: 0.967809\n", "\n", "โœ… t-SNE complete! Shape: (4000, 2)\n", "\n", "๐Ÿ’พ Saved: ../results/tsne_interactive.html\n", "\n", "๐ŸŽฏ KEY INSIGHT:\n", " If job posting bridge works โ†’ candidates & companies should overlap!\n", "================================================================================\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{text}", "marker": { "color": "#2ecc71", "line": { "width": 0 }, "opacity": 0.6, "size": 6 }, "mode": "markers", "name": "Candidates", "text": [ "Candidate 0
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Industry: Telecommunications", "Company: CDW
Industry: IT Services and IT Consulting", "Company: Textron
Industry: Aviation and Aerospace Component Manufacturing", "Company: Textron Systems
Industry: Defense and Space Manufacturing", "Company: Kaplan
Industry: Education Administration Programs", "Company: Fiserv
Industry: IT Services and IT Consulting", "Company: Nordstrom
Industry: Retail", "Company: UC San Diego
Industry: Higher Education", "Company: UC San Diego Health
Industry: Hospitals and Health Care", "Company: IDC
Industry: Market Research", "Company: Celestica
Industry: Manufacturing", "Company: FICO
Industry: Software Development", "Company: Sodexo
Industry: Facilities Services", "Company: Pizza Hut
Industry: Restaurants", "Company: Taco Bell
Industry: Restaurants", "Company: Yum! Brands
Industry: Restaurants", "Company: Georgia-Pacific LLC
Industry: Paper and Forest Product Manufacturing", "Company: New York Life Insurance Company
Industry: Financial Services", "Company: Kimberly-Clark
Industry: Manufacturing", "Company: Peace Corps
Industry: International Affairs", "Company: Analog Devices
Industry: Semiconductor Manufacturing", "Company: UPMC
Industry: Hospitals and Health Care", "Company: UPMC Health Plan
Industry: Insurance", "Company: Electrolux Group
Industry: Manufacturing", "Company: Holcim
Industry: Wholesale Building Materials", "Company: Michael Page
Industry: Staffing and Recruiting", "Company: Hays
Industry: Staffing and Recruiting", "Company: IDEMIA
Industry: Software Development", "Company: Conagra Brands
Industry: Food and Beverage Services", "Company: Progress
Industry: Software Development", "Company: Safeway
Industry: Retail", "Company: Weill Cornell Medicine
Industry: Hospitals and Health Care", "Company: Cornell University
Industry: Higher Education", "Company: Johns Hopkins Hospital
Industry: Hospitals and Health Care", "Company: The Johns Hopkins University
Industry: Higher Education", "Company: Continental
Industry: Motor Vehicle Manufacturing", "Company: Edelman
Industry: Public Relations and Communications Services", "Company: Macquarie Group
Industry: Financial Services", "Company: Red Hat
Industry: Software Development", "Company: IHG Hotels & Resorts
Industry: Hospitality", "Company: Boston University
Industry: Higher Education", "Company: Georgia Tech Research Institute
Industry: Research Services", "Company: Georgia Institute of Technology
Industry: Higher Education", "Company: Hughes
Industry: Telecommunications", "Company: Arrow Electronics
Industry: Software Development", "Company: Computacenter
Industry: IT Services and IT Consulting", "Company: Mphasis
Industry: IT Services and IT Consulting", "Company: The Princeton Group
Industry: Staffing and Recruiting", "Company: Walgreens
Industry: Retail", "Company: ESPN
Industry: Broadcast Media Production and Distribution", "Company: NVIDIA
Industry: Computer Hardware Manufacturing", "Company: Cummins Inc.
Industry: Motor Vehicle Manufacturing", "Company: HCA Healthcare
Industry: Hospitals and Health Care", "Company: HCA Healthcare Physician Services
Industry: Hospitals and Health Care", "Company: MassMutual
Industry: Financial Services", "Company: Compucom
Industry: IT Services and IT Consulting", "Company: University of Maryland
Industry: Higher Education", "Company: Lenovo
Industry: IT Services and IT Consulting", "Company: Penn State University
Industry: Higher Education", "Company: Penn State Health
Industry: Hospitals and Health Care", "Company: H&R Block
Industry: Retail", "Company: CACI International Inc
Industry: IT Services and IT Consulting", "Company: Franklin Templeton
Industry: Financial Services", "Company: Edward Jones
Industry: Financial Services", "Company: Corning Incorporated
Industry: Glass, Ceramics and Concrete Manufacturing", "Company: Fluor Corporation
Industry: Construction", "Company: Mastech Digital
Industry: IT Services and IT Consulting", "Company: JCPenney
Industry: Retail", "Company: Micron Technology
Industry: Semiconductor Manufacturing", "Company: United States Postal Service
Industry: Government Administration", "Company: Equifax
Industry: Financial Services", "Company: Lear Corporation
Industry: Motor Vehicle Manufacturing", "Company: The Reynolds and Reynolds Company
Industry: Software Development", "Company: the LEGO Group
Industry: Manufacturing", "Company: ArcelorMittal
Industry: Mining", "Company: Korn Ferry
Industry: Business Consulting and Services", "Company: RSM US LLP
Industry: Accounting", "Company: ZF Group
Industry: Motor Vehicle Manufacturing", "Company: adidas
Industry: Sporting Goods Manufacturing", "Company: University of North Carolina at Chapel Hill
Industry: Higher Education", "Company: Discover Financial Services
Industry: Financial Services", "Company: GroupM
Industry: Advertising Services", "Company: University of Colorado
Industry: Higher Education", "Company: University of Colorado Boulder
Industry: Higher Education", "Company: Marvell Technology
Industry: Semiconductor Manufacturing", "Company: Epsilon
Industry: Advertising Services", "Company: Iron Mountain
Industry: IT Services and IT Consulting", "Company: John Deere
Industry: Machinery Manufacturing", "Company: AllianceBernstein
Industry: Financial Services", "Company: Air Liquide
Industry: Chemical Manufacturing", "Company: Northern Trust
Industry: Financial Services", "Company: Swiss Re
Industry: Insurance", "Company: MITRE
Industry: IT Services and IT Consulting", "Company: DS Smith
Industry: Manufacturing", "Company: Informatica
Industry: Software Development", "Company: WebMD
Industry: Software Development", "Company: Grainger
Industry: Retail Office Equipment", "Company: FedEx Office
Industry: Printing Services", "Company: Rolls-Royce
Industry: Industrial Machinery Manufacturing", "Company: University of Chicago
Industry: Higher Education", "Company: Emory Healthcare
Industry: Hospitals and Health Care", "Company: Emory University
Industry: Higher Education", "Company: ASML
Industry: Semiconductor Manufacturing", "Company: Pacific Gas and Electric Company
Industry: Utilities", "Company: Framatome
Industry: Utilities", "Company: The Goodyear Tire & Rubber Company
Industry: Motor Vehicle Manufacturing", "Company: U.S. House of Representatives
Industry: Legislative Offices", "Company: Akamai Technologies
Industry: Software Development", "Company: Hillsborough County Public Schools
Industry: Primary and Secondary Education", "Company: Clifford Chance
Industry: Law Practice", "Company: Baker McKenzie
Industry: Law Practice", "Company: Ciena
Industry: Telecommunications", "Company: Biogen
Industry: Biotechnology Research", "Company: Heidrick & Struggles
Industry: Business Consulting and Services", "Company: Houghton Mifflin Harcourt
Industry: E-Learning Providers", "Company: WTW
Industry: Financial Services", "Company: Aflac
Industry: Insurance", "Company: Syngenta
Industry: Farming", "Company: American Cancer Society
Industry: Non-profit Organizations", "Company: Capital Group
Industry: Financial Services", "Company: Jacobs
Industry: Business Consulting and Services", "Company: Bose Corporation
Industry: Computers and Electronics Manufacturing", "Company: FMC Corporation
Industry: Chemical Manufacturing", "Company: TIAA
Industry: Financial Services", "Company: Invesco US
Industry: Financial Services", "Company: IQVIA
Industry: Hospitals and Health Care", "Company: The Estรฉe Lauder Companies Inc.
Industry: Personal Care Product Manufacturing", "Company: Cushman & Wakefield
Industry: Real Estate", "Company: Faurecia
Industry: Motor Vehicle Manufacturing", "Company: Duke Energy Corporation
Industry: Utilities", "Company: Yale School of Medicine
Industry: Research Services", "Company: Sun Life
Industry: Financial Services", "Company: DreamWorks Animation
Industry: Animation and Post-production", "Company: Tata Communications
Industry: Telecommunications", "Company: American Honda Motor Company, Inc.
Industry: Motor Vehicle Manufacturing", "Company: University of Wisconsin-Madison
Industry: Higher Education", "Company: Starcom
Industry: Advertising Services", "Company: Michelin
Industry: Motor Vehicle Manufacturing", "Company: Solvay
Industry: Chemical Manufacturing", "Company: Pottery Barn
Industry: Retail", "Company: Williams-Sonoma, Inc.
Industry: Retail", "Company: Forrester
Industry: Research Services", "Company: SGS
Industry: IT Services and IT Consulting", "Company: Lowe's Companies, Inc.
Industry: Retail", "Company: Pirelli
Industry: Motor Vehicle Manufacturing", "Company: Air Products
Industry: Chemical Manufacturing", "Company: CareerBuilder
Industry: Software Development", "Company: PNC
Industry: Financial Services", "Company: Norsk Hydro
Industry: Mining", "Company: Gannett | USA TODAY NETWORK
Industry: Media Production", "Company: Raymond James
Industry: Financial Services", "Company: Embraer
Industry: Aviation and Aerospace Component Manufacturing", "Company: Ohio Department of Transportation
Industry: Government Administration", "Company: Ohio Department of Health
Industry: Government Administration", "Company: TTEC
Industry: Outsourcing and Offshoring Consulting", "Company: Regions Bank
Industry: Banking", "Company: EMD Serono, Inc.
Industry: Pharmaceutical Manufacturing", "Company: Paychex
Industry: Human Resources Services", "Company: CAE
Industry: Aviation and Aerospace Component Manufacturing", "Company: Humana
Industry: Insurance", "Company: Rutgers University
Industry: Higher Education", "Company: Vestas
Industry: Industrial Machinery Manufacturing", "Company: UF Health Jacksonville
Industry: Hospitals and Health Care", "Company: Arizona State University
Industry: Higher Education", "Company: AMC Networks
Industry: Entertainment Providers", "Company: DISH Network
Industry: Telecommunications", "Company: UVA Health
Industry: Hospitals and Health Care", "Company: University of Virginia
Industry: Higher Education", "Company: Lincoln Financial Group
Industry: Financial Services", "Company: TransUnion
Industry: IT Services and IT Consulting", "Company: Logitech
Industry: Computers and Electronics Manufacturing", "Company: Baker Hughes
Industry: Oil and Gas", "Company: Skanska
Industry: Construction", "Company: FleishmanHillard
Industry: Public Relations and Communications Services", "Company: GfK - An NIQ Company
Industry: IT Services and IT Consulting", "Company: Sanmina
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Manhattan Associates
Industry: Software Development", "Company: Weber Shandwick
Industry: Public Relations and Communications Services", "Company: Coloplast
Industry: Medical Equipment Manufacturing", "Company: Assurant
Industry: Insurance", "Company: Principal Financial Group
Industry: Financial Services", "Company: Cemex
Industry: Wholesale Building Materials", "Company: DNV
Industry: Public Safety", "Company: Paramount Pictures
Industry: Entertainment Providers", "Company: Primerica
Industry: Financial Services", "Company: Barnes & Noble, Inc.
Industry: Retail", "Company: UCI Health
Industry: Hospitals and Health Care", "Company: DLA Piper
Industry: Law Practice", "Company: Aquent
Industry: Staffing and Recruiting", "Company: Nomura
Industry: Financial Services", "Company: Aspen Technology
Industry: Software Development", "Company: Parexel
Industry: Pharmaceutical Manufacturing", "Company: Ryder System, Inc.
Industry: Truck Transportation", "Company: Diebold Nixdorf
Industry: IT Services and IT Consulting", "Company: Weyerhaeuser
Industry: Paper and Forest Product Manufacturing", "Company: USAA
Industry: Financial Services", "Company: Scholastic
Industry: Book and Periodical Publishing", "Company: Anheuser-Busch
Industry: Food and Beverage Services", "Company: UMass Chan Medical School
Industry: Higher Education", "Company: Campbell's
Industry: Food and Beverage Manufacturing", "Company: Hearst Magazines
Industry: Media Production", "Company: Hearst
Industry: Media Production", "Company: Houston Methodist
Industry: Hospitals and Health Care", "Company: Nous Infosystems
Industry: IT Services and IT Consulting", "Company: Kuehne+Nagel
Industry: Truck Transportation", "Company: National Football League (NFL)
Industry: Spectator Sports", "Company: San Francisco 49ers
Industry: Spectator Sports", "Company: Indianapolis Colts
Industry: Spectator Sports", "Company: Texas A&M University
Industry: Higher Education", "Company: PerkinElmer
Industry: Biotechnology Research", "Company: Vanderbilt University Medical Center
Industry: Hospitals and Health Care", "Company: Vanderbilt University
Industry: Higher Education", "Company: Lundbeck
Industry: Pharmaceutical Manufacturing", "Company: Parker Aerospace
Industry: Aviation and Aerospace Component Manufacturing", "Company: Parker Hannifin
Industry: Industrial Machinery Manufacturing", "Company: SKF Group
Industry: Industrial Machinery Manufacturing", "Company: Western Digital
Industry: Computer Hardware Manufacturing", "Company: Southwest Airlines
Industry: Airlines and Aviation", "Company: Allen & Overy
Industry: Law Practice", "Company: Washington University in St. Louis
Industry: Higher Education", "Company: Massachusetts Department of Public Health
Industry: Government Administration", "Company: Epicor
Industry: Software Development", "Company: CNH Industrial
Industry: Machinery Manufacturing", "Company: The George Washington University
Industry: Higher Education", "Company: American Family Insurance
Industry: Insurance", "Company: FDA
Industry: Government Administration", "Company: Birlasoft
Industry: IT Services and IT Consulting", "Company: The North Face
Industry: Retail Apparel and Fashion", "Company: Nautica
Industry: Retail Apparel and Fashion", "Company: VF Corporation
Industry: Retail Apparel and Fashion", "Company: Exelon
Industry: Utilities", "Company: CVS Health
Industry: Hospitals and Health Care", "Company: Takeda Oncology
Industry: Pharmaceutical Manufacturing", "Company: Ralph Lauren
Industry: Retail Apparel and Fashion", "Company: Spirent Communications
Industry: Telecommunications", "Company: Brown Brothers Harriman
Industry: Financial Services", "Company: The Church of Jesus Christ of Latter-day Saints
Industry: Religious Institutions", "Company: Ecolab
Industry: Chemical Manufacturing", "Company: Pacific Northwest National Laboratory
Industry: Research Services", "Company: UL Solutions
Industry: International Trade and Development, IT Services a", "Company: Mayo Clinic
Industry: Hospitals and Health Care", "Company: Quest Diagnostics
Industry: Hospitals and Health Care", "Company: NICE
Industry: Software Development", "Company: UScellular
Industry: Telecommunications", "Company: Social Security Administration
Industry: Government Administration", "Company: Lazard
Industry: Financial Services", "Company: BlackRock
Industry: Financial Services", "Company: Deluxe
Industry: Financial Services", "Company: Freshfields Bruckhaus Deringer
Industry: Law Practice", "Company: IFC - International Finance Corporation
Industry: Financial Services", "Company: Apex Systems
Industry: IT Services and IT Consulting", "Company: Smith+Nephew
Industry: Medical Equipment Manufacturing", "Company: Hallmark Cards
Industry: Retail", "Company: Atlas Copco
Industry: Machinery Manufacturing", "Company: North Carolina State University
Industry: Higher Education", "Company: DB Schenker
Industry: Truck Transportation", "Company: SEI
Industry: Financial Services", "Company: JTI (Japan Tobacco International)
Industry: Tobacco Manufacturing", "Company: Konica Minolta Business Solutions U.S.A., Inc.
Industry: Software Development", "Company: U.S. Department of Commerce
Industry: Government Administration", "Company: F5
Industry: IT Services and IT Consulting", "Company: Jabil
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: EF Education First
Industry: Education Administration Programs", "Company: PPG
Industry: Chemical Manufacturing", "Company: Skadden, Arps, Slate, Meagher & Flom LLP and Affiliates
Industry: Law Practice", "Company: Harvard Business School
Industry: Higher Education", "Company: UNICEF
Industry: Non-profit Organizations", "Company: Pulte Mortgage
Industry: Financial Services", "Company: PulteGroup
Industry: Construction", "Company: University of Utah Health
Industry: Hospitals and Health Care", "Company: Blue Shield of California
Industry: Insurance", "Company: Parsons Corporation
Industry: IT Services and IT Consulting", "Company: Specialty Equipment Market Association (SEMA)
Industry: Motor Vehicle Manufacturing", "Company: Kroger
Industry: Retail", "Company: Metso
Industry: Industrial Machinery Manufacturing", "Company: White & Case LLP
Industry: Law Practice", "Company: Internal Revenue Service
Industry: Government Administration", "Company: Otis Elevator Co.
Industry: Consumer Services", "Company: Latham & Watkins
Industry: Law Practice", "Company: Hasbro
Industry: Manufacturing", "Company: Reebok
Industry: Manufacturing", "Company: Porter Novelli
Industry: Public Relations and Communications Services", "Company: Bankers Trust
Industry: Financial Services", "Company: AMERICAN EAGLE OUTFITTERS INC.
Industry: Retail", "Company: EPAM Systems
Industry: IT Services and IT Consulting", "Company: Temple Health โ€“ Temple University Health System
Industry: Hospitals and Health Care", "Company: Phoenix Technologies
Industry: Software Development", "Company: Lawrence Livermore National Laboratory
Industry: Research Services", "Company: Cartier
Industry: Retail Luxury Goods and Jewelry", "Company: CNO Financial Group
Industry: Insurance", "Company: Bankers Life
Industry: Insurance", "Company: University of Rochester Medical Center
Industry: Hospitals and Health Care", "Company: University of Rochester
Industry: Higher Education", "Company: News Corp
Industry: Software Development", "Company: Persistent Systems
Industry: IT Services and IT Consulting", "Company: Federal Aviation Administration
Industry: Aviation and Aerospace Component Manufacturing", "Company: USAID
Industry: International Affairs", "Company: Cintas
Industry: Facilities Services", "Company: KONE
Industry: Industrial Machinery Manufacturing", "Company: Alfa Laval
Industry: Industrial Machinery Manufacturing", "Company: Sophos
Industry: Software Development", "Company: Ketchum
Industry: Public Relations and Communications Services", "Company: Intelsat
Industry: Telecommunications", "Company: CHEP
Industry: Truck Transportation", "Company: Dana Incorporated
Industry: Motor Vehicle Manufacturing", "Company: Southern Company
Industry: Utilities", "Company: Jones Day
Industry: Law Practice", "Company: Ticketmaster
Industry: Entertainment Providers", "Company: aramco
Industry: Oil and Gas", "Company: Lam Research
Industry: Semiconductor Manufacturing", "Company: Acer
Industry: Computer Hardware Manufacturing", "Company: Navistar Inc
Industry: Motor Vehicle Manufacturing", "Company: Constellation
Industry: Utilities", "Company: The TJX Companies, Inc.
Industry: Retail", "Company: Nasdaq
Industry: Financial Services", "Company: Anritsu
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Virtusa
Industry: IT Services and IT Consulting", "Company: IGT
Industry: Software Development", "Company: Vestcom
Industry: Advertising Services", "Company: Jefferies
Industry: Investment Banking", "Company: Trimble Inc.
Industry: Software Development", "Company: Morningstar
Industry: Financial Services", "Company: Los Angeles Times
Industry: Newspaper Publishing", "Company: Sandvik
Industry: Mining", "Company: Sandvik Coromant
Industry: Industrial Machinery Manufacturing", "Company: Bausch + Lomb
Industry: Pharmaceutical Manufacturing", "Company: Ascensus
Industry: Financial Services", "Company: GRUNDFOS
Industry: Industrial Machinery Manufacturing", "Company: L.E.K. Consulting
Industry: Business Consulting and Services", "Company: Teleperformance
Industry: Outsourcing and Offshoring Consulting", "Company: Nalco Water, An Ecolab Company
Industry: Chemical Manufacturing", "Company: Colliers
Industry: Real Estate", "Company: Rackspace Technology
Industry: IT Services and IT Consulting", "Company: LHH
Industry: Human Resources Services", "Company: ZS
Industry: Business Consulting and Services", "Company: HCLTech - SAP Practice
Industry: Business Consulting and Services", "Company: DSV - Global Transport and Logistics
Industry: Truck Transportation", "Company: McMaster-Carr
Industry: Truck Transportation", "Company: SRI
Industry: Research Services", "Company: Northeastern University
Industry: Higher Education", "Company: Alcon
Industry: Medical Equipment Manufacturing", "Company: Rent.
Industry: Software Development", "Company: LPL Financial
Industry: Financial Services", "Company: Luxoft
Industry: IT Services and IT Consulting", "Company: Esri
Industry: Software Development", "Company: Owens Corning
Industry: Wholesale Building Materials", "Company: Tenet Healthcare
Industry: Hospitals and Health Care", "Company: MFS Investment Management
Industry: Financial Services", "Company: ALTEN
Industry: IT Services and IT Consulting", "Company: Los Alamos National Laboratory
Industry: Research Services", "Company: H&M
Industry: Retail", "Company: Blizzard Entertainment
Industry: Entertainment Providers", "Company: Sysco
Industry: Food and Beverage Services", "Company: Softtek
Industry: IT Services and IT Consulting", "Company: Connection
Industry: IT Services and IT Consulting", "Company: GSA
Industry: Government Administration", "Company: G4S
Industry: Security and Investigations", "Company: UC Santa Barbara
Industry: Higher Education", "Company: VELUX
Industry: Wholesale Building Materials", "Company: Sandia National Laboratories
Industry: Defense and Space Manufacturing", "Company: The Hanover Insurance Group
Industry: Insurance", "Company: Abercrombie & Fitch Co.
Industry: Retail", "Company: University of Missouri Health Care
Industry: Hospitals and Health Care", "Company: University of Missouri-Columbia
Industry: Higher Education", "Company: Hess Corporation
Industry: Oil and Gas", "Company: ManTech
Industry: IT Services and IT Consulting", "Company: Ashland
Industry: Chemical Manufacturing", "Company: Capco
Industry: Financial Services", "Company: GALLO
Industry: Beverage Manufacturing", "Company: Ferrero
Industry: Manufacturing", "Company: Takeda
Industry: Pharmaceutical Manufacturing", "Company: Mercatus Center at George Mason University
Industry: Research Services", "Company: George Mason University
Industry: Higher Education", "Company: CME Group
Industry: Financial Services", "Company: Consilio LLC
Industry: Law Practice", "Company: Crowe
Industry: Accounting", "Company: Morrison Foerster
Industry: Law Practice", "Company: FTI Consulting
Industry: Business Consulting and Services", "Company: Concurrent Technologies Corporation
Industry: Defense and Space Manufacturing", "Company: Southern Methodist University
Industry: Higher Education", "Company: KPIT
Industry: Software Development", "Company: Westfield Insurance
Industry: Insurance", "Company: Premera Blue Cross
Industry: Insurance", "Company: ADM
Industry: Food and Beverage Manufacturing", "Company: The Standard
Industry: Financial Services", "Company: Plexus Corp.
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Allied Telesis
Industry: Computer Networking Products", "Company: Group Lotus
Industry: Motor Vehicle Manufacturing", "Company: Nintendo
Industry: Computer Games", "Company: Forbes
Industry: Book and Periodical Publishing", "Company: USANA Health Sciences
Industry: Wellness and Fitness Services", "Company: Mercury Systems
Industry: Defense and Space Manufacturing", "Company: Capella University
Industry: Higher Education", "Company: Greenberg Traurig, LLP
Industry: Law Practice", "Company: Cirrus Logic
Industry: Semiconductor Manufacturing", "Company: Coach
Industry: Retail Apparel and Fashion", "Company: Herbalife
Industry: Wellness and Fitness Services", "Company: McClatchy
Industry: Newspaper Publishing", "Company: Varian
Industry: Medical Equipment Manufacturing", "Company: Memorial Sloan Kettering Cancer Center
Industry: Hospitals and Health Care", "Company: Huntsman Corporation
Industry: Chemical Manufacturing", "Company: Cleveland Clinic
Industry: Hospitals and Health Care", "Company: Dominion Energy
Industry: Utilities", "Company: LyondellBasell
Industry: Chemical Manufacturing", "Company: Kohler Co.
Industry: Manufacturing", "Company: Cooper University Health Care
Industry: Hospitals and Health Care", "Company: CJ
Industry: Advertising Services", "Company: Netia
Industry: Telecommunications", "Company: U.S. Department of Labor
Industry: Government Administration", "Company: ACCO Brands
Industry: Manufacturing", "Company: Santander Bank, N.A.
Industry: Banking", "Company: Sasol
Industry: Chemical Manufacturing", "Company: University of Denver
Industry: Higher Education", "Company: Ball Corporation
Industry: Packaging and Containers Manufacturing", "Company: Kirkland & Ellis
Industry: Law Practice", "Company: Morgan, Lewis & Bockius LLP
Industry: Law Practice", "Company: ICF
Industry: Business Consulting and Services", "Company: Kohl's
Industry: Retail", "Company: CPS, Inc.
Industry: Staffing and Recruiting", "Company: Huron
Industry: Business Consulting and Services", "Company: Penske Logistics
Industry: Truck Transportation", "Company: Penske Truck Leasing
Industry: Truck Transportation", "Company: SPX Cooling Tech, LLC
Industry: Industrial Machinery Manufacturing", "Company: Viasat
Industry: Telecommunications", "Company: Turner Construction Company
Industry: Construction", "Company: Univision
Industry: Broadcast Media Production and Distribution", "Company: Louis Vuitton
Industry: Retail Luxury Goods and Jewelry", "Company: Kerry
Industry: Food and Beverage Services", "Company: Cobham Satcom
Industry: Telecommunications", "Company: Gensler
Industry: Architecture and Planning", "Company: Moss Adams
Industry: Accounting", "Company: RTI International
Industry: Research Services", "Company: Tommy Hilfiger
Industry: Retail Apparel and Fashion", "Company: Hogan Lovells
Industry: Law Practice", "Company: Fairfax County Public Schools
Industry: Primary and Secondary Education", "Company: ICON plc
Industry: Biotechnology Research", "Company: Orrick, Herrington & Sutcliffe LLP
Industry: Law Practice", "Company: Omron Automation
Industry: Automation Machinery Manufacturing", "Company: Arcadis
Industry: IT Services and IT Consulting", "Company: Johns Manville
Industry: Manufacturing", "Company: Tennessee Valley Authority
Industry: Utilities", "Company: Valassis Marketing Solutions
Industry: Advertising Services", "Company: Federal Highway Administration
Industry: Government Administration", "Company: U.S. Department of Transportation
Industry: Government Administration", "Company: IFF
Industry: Chemical Manufacturing", "Company: Smithsonian Enterprises
Industry: Museums, Historical Sites, and Zoos", "Company: MKS Instruments
Industry: Semiconductor Manufacturing", "Company: Motion Recruitment
Industry: Staffing and Recruiting", "Company: TDS Telecommunications LLC
Industry: Telecommunications", "Company: Cubic Corporation
Industry: IT Services and IT Consulting", "Company: National Grid
Industry: Utilities", "Company: O'Melveny & Myers LLP
Industry: Law Practice", "Company: University of Nebraska Foundation
Industry: Fundraising", "Company: University of Nebraska-Lincoln
Industry: Higher Education", "Company: Flowserve Corporation
Industry: Industrial Machinery Manufacturing", "Company: Quadrangle
Industry: Staffing and Recruiting", "Company: Autoliv
Industry: Motor Vehicle Manufacturing", "Company: Boston Public Schools
Industry: Education Administration Programs", "Company: Armstrong World Industries
Industry: Wholesale Building Materials", "Company: Chr. Hansen
Industry: Biotechnology Research", "Company: FM Global
Industry: Insurance", "Company: Fresenius Medical Care North America
Industry: Hospitals and Health Care", "Company: Union Pacific Railroad
Industry: Truck Transportation", "Company: W. L. Gore & Associates
Industry: Medical Equipment Manufacturing", "Company: University of Kentucky
Industry: Higher Education", "Company: Cooley LLP
Industry: Law Practice", "Company: Michaels Stores
Industry: Retail", "Company: Yoh, A Day & Zimmermann Company
Industry: Staffing and Recruiting", "Company: Mayer Brown
Industry: Law Practice", "Company: Choice Hotels International
Industry: Hospitality", "Company: Rensselaer Polytechnic Institute
Industry: Higher Education", "Company: Advantage Technical
Industry: Staffing and Recruiting", "Company: CBIZ
Industry: Business Consulting and Services", "Company: Lands'โ€‹ End
Industry: Retail Apparel and Fashion", "Company: AppleOne Employment Services
Industry: Staffing and Recruiting", "Company: UNSW
Industry: Higher Education", "Company: NYU Langone Health
Industry: Hospitals and Health Care", "Company: Atlanticus
Industry: Financial Services", "Company: NetSuite
Industry: Software Development", "Company: Agilysys
Industry: Software Development", "Company: County of Santa Clara
Industry: Government Administration", "Company: Icahn School of Medicine at Mount Sinai
Industry: Hospitals and Health Care", "Company: Match
Industry: Software Development", "Company: 7-Eleven
Industry: Retail", "Company: Zions Bancorporation
Industry: Banking", "Company: Schindler Group
Industry: Industrial Machinery Manufacturing", "Company: Schindler Elevator Corporation (U.S.)
Industry: Industrial Machinery Manufacturing", "Company: Brunswick Corporation
Industry: Manufacturing", "Company: ePlus inc.
Industry: IT Services and IT Consulting", "Company: Brady Corporation
Industry: Manufacturing", "Company: FUJIFILM Holdings America Corporation
Industry: Executive Offices", "Company: Fresche Solutions
Industry: IT Services and IT Consulting", "Company: AMS
Industry: Human Resources Services", "Company: Thrivent
Industry: Financial Services", "Company: Schwan's Company
Industry: Food and Beverage Manufacturing", "Company: Baylor Scott & White Health
Industry: Hospitals and Health Care", "Company: The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology
Industry: Biotechnology Research", "Company: CSX
Industry: Truck Transportation", "Company: Crain Communications
Industry: Book and Periodical Publishing", "Company: Oklahoma State University
Industry: Higher Education", "Company: Infogain
Industry: IT Services and IT Consulting", "Company: QuinStreet
Industry: Technology, Information and Internet", "Company: Landis+Gyr
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Safety-Kleen
Industry: Environmental Services", "Company: Paul Hastings
Industry: Law Practice", "Company: Reed Smith LLP
Industry: Law Practice", "Company: Sutherland
Industry: IT Services and IT Consulting", "Company: Marcus & Millichap
Industry: Real Estate", "Company: Georgia State University
Industry: Higher Education", "Company: National Institute of Standards and Technology (NIST)
Industry: Research Services", "Company: NewYork-Presbyterian Hospital
Industry: Hospitals and Health Care", "Company: Drรคger
Industry: Manufacturing", "Company: Tata Technologies
Industry: IT Services and IT Consulting", "Company: Russell Investments
Industry: Financial Services", "Company: MSCI Inc.
Industry: Financial Services", "Company: Axis Communications
Industry: Software Development", "Company: Federal Bureau of Investigation (FBI)
Industry: Law Enforcement", "Company: Oregon Department of Transportation
Industry: Government Administration", "Company: Altria
Industry: Manufacturing", "Company: HARMAN International
Industry: Computers and Electronics Manufacturing", "Company: Kofax
Industry: Software Development", "Company: Zimmer Biomet
Industry: Medical Equipment Manufacturing", "Company: ERM
Industry: Business Consulting and Services", "Company: Fortinet
Industry: Computer and Network Security", "Company: Loyola Medicine
Industry: Hospitals and Health Care", "Company: Loyola University Chicago
Industry: Higher Education", "Company: Microchip Technology Inc.
Industry: Semiconductor Manufacturing", "Company: Greyhound Lines, Inc.
Industry: Truck Transportation", "Company: DaVita Kidney Care
Industry: Hospitals and Health Care", "Company: Tetra Tech
Industry: Civil Engineering", "Company: 24 Hour Fitness
Industry: Wellness and Fitness Services", "Company: UT Southwestern Medical Center
Industry: Hospitals and Health Care", "Company: Airlines Reporting Corporation (ARC)
Industry: Airlines and Aviation", "Company: Select Medical
Industry: Hospitals and Health Care", "Company: Boston Globe Media
Industry: Online Audio and Video Media", "Company: Ansys
Industry: Software Development", "Company: Connexity, Inc.
Industry: Software Development", "Company: Gallagher
Industry: Insurance", "Company: SoftServe
Industry: IT Services and IT Consulting", "Company: Sappi
Industry: Paper and Forest Product Manufacturing", "Company: UMB Bank
Industry: Financial Services", "Company: Advocate Health Care
Industry: Hospitals and Health Care", "Company: Shure Incorporated
Industry: Computers and Electronics Manufacturing", "Company: Gucci
Industry: Retail Luxury Goods and Jewelry", "Company: MSA - The Safety Company
Industry: Public Safety", "Company: Norfolk Southern
Industry: Truck Transportation", "Company: Live Nation Entertainment
Industry: Entertainment Providers", "Company: dunnhumby
Industry: Advertising Services", "Company: AMERICAN SYSTEMS
Industry: IT Services and IT Consulting", "Company: HOK
Industry: Architecture and Planning", "Company: Gibson Dunn
Industry: Law Practice", "Company: Marathon Oil Corporation
Industry: Oil and Gas", "Company: Pro Staff
Industry: Staffing and Recruiting", "Company: Eisai US
Industry: Pharmaceutical Manufacturing", "Company: Worley
Industry: IT Services and IT Consulting", "Company: Hollister Incorporated
Industry: Medical Equipment Manufacturing", "Company: Rockstar Games
Industry: Computer Games", "Company: ClubCorp
Industry: Hospitality", "Company: CAS
Industry: IT Services and IT Consulting", "Company: Enbridge
Industry: Oil and Gas", "Company: Pall Corporation
Industry: Manufacturing", "Company: MSNBC
Industry: Broadcast Media Production and Distribution", "Company: J.Crew
Industry: Retail Apparel and Fashion", "Company: WVU Medicine
Industry: Hospitals and Health Care", "Company: Philadelphia Housing Authority
Industry: Real Estate", "Company: Austin Independent School District
Industry: Primary and Secondary Education", "Company: Incyte
Industry: Pharmaceutical Manufacturing", "Company: Premier Inc.
Industry: Hospitals and Health Care", "Company: B. Braun Medical Inc. (US)
Industry: Medical Equipment Manufacturing", "Company: MultiPlan
Industry: Hospitals and Health Care", "Company: Sirva
Industry: Real Estate", "Company: Tulane University
Industry: Higher Education", "Company: SEPHORA
Industry: Retail", "Company: Southern Glazer's Wine & Spirits
Industry: Beverage Manufacturing", "Company: Bitdefender
Industry: Software Development", "Company: Milliken & Company
Industry: Industrial Machinery Manufacturing", "Company: State of Indiana
Industry: Government Administration", "Company: Highmark Inc.
Industry: Insurance", "Company: AEG
Industry: Entertainment Providers", "Company: Devon Energy
Industry: Oil and Gas", "Company: Dice
Industry: Software Development", "Company: Simpson Thacher & Bartlett LLP
Industry: Law Practice", "Company: Marriott Vacations Worldwide
Industry: Hospitality", "Company: Vertex Pharmaceuticals
Industry: Biotechnology Research", "Company: Align Technology
Industry: Medical Equipment Manufacturing", "Company: Hennepin County
Industry: Government Administration", "Company: American Residential Services
Industry: Consumer Services", "Company: Delta Dental Ins.
Industry: Insurance", "Company: American Institutes for Research
Industry: Research Services", "Company: BJC HealthCare
Industry: Hospitals and Health Care", "Company: Florida International University
Industry: Higher Education", "Company: RITE AID
Industry: Retail", "Company: The Depository Trust & Clearing Corporation (DTCC)
Industry: Financial Services", "Company: Strategic Staffing Solutions
Industry: IT Services and IT Consulting", "Company: Akin Gump Strauss Hauer & Feld LLP
Industry: Law Practice", "Company: Holland & Knight LLP
Industry: Law Practice", "Company: Hibu
Industry: Advertising Services", "Company: InterSystems
Industry: Software Development", "Company: Benchmark
Industry: Computers and Electronics Manufacturing", "Company: Jack Henry
Industry: Financial Services", "Company: Federal Deposit Insurance Corporation (FDIC)
Industry: Banking", "Company: Carnival Corporation
Industry: Travel Arrangements", "Company: Intertek
Industry: International Trade and Development", "Company: Baird
Industry: Financial Services", "Company: Indotronix International Corporation
Industry: Staffing and Recruiting", "Company: International SOS
Industry: Hospitals and Health Care", "Company: Columbia Sportswear Company
Industry: Retail Apparel and Fashion", "Company: CoStar Group
Industry: Real Estate", "Company: Ross Stores, Inc.
Industry: Retail", "Company: IEEE
Industry: Non-profit Organizations", "Company: Response Companies
Industry: Staffing and Recruiting", "Company: Rohde & Schwarz
Industry: Telecommunications", "Company: Mercy
Industry: Hospitals and Health Care", "Company: State of Tennessee
Industry: Government Administration", "Company: Ruder Finn
Industry: Public Relations and Communications Services", "Company: Meijer
Industry: Retail", "Company: American Medical Association
Industry: Non-profit Organizations", "Company: Auburn University
Industry: Higher Education", "Company: RS
Industry: Truck Transportation", "Company: SGK
Industry: Advertising Services", "Company: Tag
Industry: Media Production", "Company: Spectrum Brands, Inc
Industry: Manufacturing", "Company: Nature Portfolio
Industry: Book and Periodical Publishing", "Company: NRG Energy
Industry: Consumer Services", "Company: American Bar Association
Industry: Law Practice", "Company: NYS Department of Transportation
Industry: Civil Engineering", "Company: The University of Texas at San Antonio
Industry: Higher Education", "Company: Fred Hutch
Industry: Hospitals and Health Care", "Company: PDS Tech Commercial, Inc.
Industry: Staffing and Recruiting", "Company: Plastic Omnium
Industry: Motor Vehicle Manufacturing", "Company: Gates Corporation
Industry: Automation Machinery Manufacturing", "Company: Acuity Brands
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: CEI
Industry: IT Services and IT Consulting", "Company: Bekaert
Industry: Manufacturing", "Company: Norton Rose Fulbright
Industry: Law Practice", "Company: Jostens
Industry: Manufacturing", "Company: CHS Inc.
Industry: Food and Beverage Manufacturing", "Company: Publix Super Markets
Industry: Retail", "Company: The Johns Hopkins University Applied Physics Laboratory
Industry: Defense and Space Manufacturing", "Company: Mott MacDonald
Industry: Civil Engineering", "Company: University of New Hampshire
Industry: Higher Education", "Company: Ultimate Staffing
Industry: Staffing and Recruiting", "Company: Brown-Forman
Industry: Beverage Manufacturing", "Company: Planview
Industry: Software Development", "Company: Sonoco
Industry: Packaging and Containers Manufacturing", "Company: Academy of Art University
Industry: Higher Education", "Company: Sunrise Senior Living
Industry: Hospitals and Health Care", "Company: Essendant
Industry: Wholesale", "Company: Mizuho
Industry: Financial Services", "Company: TC Transcontinental
Industry: Packaging and Containers Manufacturing", "Company: Fordham University
Industry: Higher Education", "Company: Linedata
Industry: Software Development", "Company: Orica
Industry: Mining", "Company: Huxley
Industry: Staffing and Recruiting", "Company: Blue Cross Blue Shield of Michigan
Industry: Insurance", "Company: Comscore, Inc.
Industry: Software Development", "Company: Domino's
Industry: Restaurants", "Company: The Leukemia & Lymphoma Society
Industry: Non-profit Organizations", "Company: Crane Aerospace & Electronics
Industry: Aviation and Aerospace Component Manufacturing", "Company: The Carlyle Group
Industry: Financial Services", "Company: CSL
Industry: Biotechnology Research", "Company: TEAM LEWIS
Industry: Advertising Services", "Company: Xcel Energy
Industry: Utilities", "Company: TC Energy
Industry: Oil and Gas", "Company: AutoZone
Industry: Retail", "Company: Boston Medical Center (BMC)
Industry: Hospitals and Health Care", "Company: NETGEAR
Industry: Software Development", "Company: Woodward, Inc.
Industry: Aviation and Aerospace Component Manufacturing", "Company: UHY LLP, Certified Public Accountants
Industry: Accounting", "Company: Brown & Brown Insurance
Industry: Insurance", "Company: Vishay Intertechnology, Inc.
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Priceline
Industry: Technology, Information and Internet", "Company: Davis Polk & Wardwell LLP
Industry: Law Practice", "Company: Dillard's Inc.
Industry: Retail", "Company: Lonza
Industry: Pharmaceutical Manufacturing", "Company: FirstEnergy
Industry: Utilities", "Company: GM Financial
Industry: Financial Services", "Company: Oakley
Industry: Retail Apparel and Fashion", "Company: D.R. Horton
Industry: Construction", "Company: RUSH University Medical Center
Industry: Hospitals and Health Care", "Company: Barry Callebaut Group
Industry: Food and Beverage Manufacturing", "Company: Bulgari
Industry: Retail Luxury Goods and Jewelry", "Company: Cedars-Sinai
Industry: Hospitals and Health Care", "Company: Illumina
Industry: Biotechnology Research", "Company: Inova Health
Industry: Hospitals and Health Care", "Company: Maryland State Highway Administration
Industry: Civil Engineering", "Company: Horizon Blue Cross Blue Shield of New Jersey
Industry: Insurance", "Company: Lockton
Industry: Insurance", "Company: Nexans
Industry: Manufacturing", "Company: ECCO
Industry: Retail Apparel and Fashion", "Company: Itron, Inc.
Industry: Utilities", "Company: Newsweek
Industry: Online Audio and Video Media", "Company: Sam's Club
Industry: Retail", "Company: Corestaff Services
Industry: Staffing and Recruiting", "Company: McDermott International, Ltd
Industry: Oil and Gas", "Company: Lennox
Industry: Manufacturing", "Company: Aurora Health Care
Industry: Hospitals and Health Care", "Company: Daiichi Sankyo US
Industry: Pharmaceutical Manufacturing", "Company: St. Jude Children's Research Hospital - ALSAC
Industry: Non-profit Organizations", "Company: State of North Carolina
Industry: Government Administration", "Company: The Timken Company
Industry: Industrial Machinery Manufacturing", "Company: University of Louisville
Industry: Higher Education", "Company: Johnson Matthey
Industry: Chemical Manufacturing", "Company: Vistage Worldwide, Inc.
Industry: Professional Training and Coaching", "Company: Cirque du Soleil Entertainment Group
Industry: Entertainment Providers", "Company: Habitat for Humanity International
Industry: Non-profit Organizations", "Company: SS&C Technologies
Industry: Software Development", "Company: Zones, LLC
Industry: IT Services and IT Consulting", "Company: Scientific Research Corporation
Industry: Defense and Space Manufacturing", "Company: University of California, Riverside
Industry: Higher Education", "Company: National General
Industry: Insurance", "Company: Emmis Corporation
Industry: Venture Capital and Private Equity Principals", "Company: GEODIS
Industry: Truck Transportation", "Company: Presidio
Industry: IT Services and IT Consulting", "Company: University of Arkansas
Industry: Higher Education", "Company: EBSCO Information Services
Industry: Information Services", "Company: NVR, Inc.
Industry: Construction", "Company: AlixPartners
Industry: Business Consulting and Services", "Company: DICK'S Sporting Goods
Industry: Retail", "Company: Petco
Industry: Retail", "Company: Riverbed Technology
Industry: Software Development", "Company: Nelson Connects
Industry: Staffing and Recruiting", "Company: Space Dynamics Laboratory
Industry: Defense and Space Manufacturing", "Company: Stevens Institute of Technology
Industry: Higher Education", "Company: Blackstone
Industry: Financial Services", "Company: University of Maryland Baltimore County
Industry: Higher Education", "Company: OUTFRONT Media
Industry: Advertising Services", "Company: STERIS
Industry: Medical Equipment Manufacturing", "Company: Model N
Industry: Software Development", "Company: TRC Companies, Inc.
Industry: Environmental Services", "Company: BorgWarner
Industry: Motor Vehicle Manufacturing", "Company: Proskauer Rose LLP
Industry: Law Practice", "Company: International Rescue Committee
Industry: Non-profit Organizations", "Company: Land O'Lakes, Inc.
Industry: Food and Beverage Manufacturing", "Company: Merkle
Industry: Business Consulting and Services", "Company: Texas Health Resources
Industry: Hospitals and Health Care", "Company: The Children's Place
Industry: Retail", "Company: Popular Bank
Industry: Financial Services", "Company: IDEXX
Industry: Biotechnology Research", "Company: PIMCO
Industry: Financial Services", "Company: Sword Group
Industry: IT Services and IT Consulting", "Company: Entrust
Industry: Software Development", "Company: Exelixis
Industry: Biotechnology Research", "Company: GHX
Industry: Hospitals and Health Care", "Company: The Lubrizol Corporation
Industry: Chemical Manufacturing", "Company: Milliman
Industry: Business Consulting and Services", "Company: State of Missouri
Industry: Government Administration", "Company: DAT Freight & Analytics
Industry: Truck Transportation", "Company: Mount Sinai Health System
Industry: Hospitals and Health Care", "Company: Life Time Inc.
Industry: Wellness and Fitness Services", "Company: Culver Careers (CulverCareers.com)
Industry: Staffing and Recruiting", "Company: GES - Global Experience Specialists
Industry: Events Services", "Company: Guy Carpenter
Industry: Insurance", "Company: Mintz
Industry: Law Practice", "Company: AMETEK
Industry: Manufacturing", "Company: Littler
Industry: Law Practice", "Company: Subway
Industry: Restaurants", "Company: Acosta
Industry: Retail", "Company: American Tower
Industry: Telecommunications", "Company: Bentley University
Industry: Higher Education", "Company: Church & Dwight Co., Inc.
Industry: Manufacturing", "Company: Deutsche Bahn
Industry: Truck Transportation", "Company: The Judge Group
Industry: Staffing and Recruiting", "Company: Unit4
Industry: Software Development", "Company: Huber Engineered Materials
Industry: Chemical Manufacturing", "Company: Globant
Industry: IT Services and IT Consulting", "Company: Orkin
Industry: Consumer Services", "Company: Master Electronics
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Staffmark
Industry: Staffing and Recruiting", "Company: Cartus
Industry: Real Estate", "Company: Quad
Industry: Advertising Services", "Company: James Hardie
Industry: Wholesale Building Materials", "Company: tms
Industry: Business Consulting and Services", "Company: Transocean
Industry: Oil and Gas", "Company: Dollar General
Industry: Retail", "Company: Callaway Golf
Industry: Retail", "Company: Equinix
Industry: Technology, Information and Internet", "Company: Pactiv Evergreen Inc.
Industry: Packaging and Containers Manufacturing", "Company: Procom
Industry: IT Services and IT Consulting", "Company: Fish & Richardson P.C.
Industry: Law Practice", "Company: New Balance
Industry: Retail", "Company: O-I
Industry: Glass, Ceramics and Concrete Manufacturing", "Company: QIAGEN
Industry: Biotechnology Research", "Company: Urban Outfitters
Industry: Retail Apparel and Fashion", "Company: Anthropologie
Industry: Retail Apparel and Fashion", "Company: Leonardo DRS
Industry: Defense and Space Manufacturing", "Company: Talbots
Industry: Retail", "Company: ATR International
Industry: Staffing and Recruiting", "Company: Banner Health
Industry: Hospitals and Health Care", "Company: Charles River Laboratories
Industry: Biotechnology Research", "Company: Husky Technologies
Industry: Machinery Manufacturing", "Company: Altair
Industry: Software Development", "Company: Sumitomo Mitsui Banking Corporation โ€“ SMBC Group
Industry: Financial Services", "Company: University of Alaska Fairbanks
Industry: Higher Education", "Company: Alston & Bird
Industry: Law Practice", "Company: Munich Re
Industry: Insurance", "Company: Dyson
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: The Guitar Center Company
Industry: Retail", "Company: MoneyGram International
Industry: Financial Services", "Company: Douglas Elliman Real Estate
Industry: Real Estate", "Company: Teleflex
Industry: Medical Equipment Manufacturing", "Company: Levi, Ray & Shoup, Inc. (LRS)
Industry: IT Services and IT Consulting", "Company: JSI
Industry: Non-profit Organizations", "Company: Municipality of Anchorage
Industry: Government Relations Services", "Company: OhioHealth
Industry: Hospitals and Health Care", "Company: BJ's Wholesale Club
Industry: Retail", "Company: The Toro Company
Industry: Machinery Manufacturing", "Company: CEVA Logistics
Industry: Truck Transportation", "Company: GKN Automotive
Industry: Motor Vehicle Manufacturing", "Company: Bowling Green State University
Industry: Higher Education", "Company: CITGO
Industry: Oil and Gas", "Company: COUNTRY Financialยฎ
Industry: Insurance", "Company: Flagstar Bank
Industry: Banking", "Company: National Car Rental
Industry: Travel Arrangements", "Company: Alamo Rent A Car
Industry: Travel Arrangements", "Company: Boral
Industry: Construction", "Company: Molson Coors Beverage Company
Industry: Food and Beverage Services", "Company: Syniverse
Industry: IT Services and IT Consulting", "Company: YASH Technologies
Industry: IT Services and IT Consulting", "Company: Calix
Industry: Software Development", "Company: Mandarin Oriental Hotel Group
Industry: Hospitality", "Company: Ipsen
Industry: Pharmaceutical Manufacturing", "Company: Entegris
Industry: Semiconductor Manufacturing", "Company: Lectra
Industry: IT Services and IT Consulting", "Company: Lionsgate
Industry: Entertainment Providers", "Company: University of Rhode Island
Industry: Higher Education", "Company: Federated Hermes
Industry: Financial Services", "Company: Lifespan
Industry: Hospitals and Health Care", "Company: Qualys
Industry: Computer and Network Security", "Company: Briggs & Stratton
Industry: Manufacturing", "Company: California State University, Fullerton
Industry: Higher Education", "Company: Gulfstream Aerospace
Industry: Aviation and Aerospace Component Manufacturing", "Company: Colonial Life
Industry: Insurance", "Company: Huhtamaki
Industry: Packaging and Containers Manufacturing", "Company: SWAROVSKI
Industry: Retail Luxury Goods and Jewelry", "Company: Brother USA
Industry: Computers and Electronics Manufacturing", "Company: National MS Society
Industry: Non-profit Organizations", "Company: Tate & Lyle
Industry: Food and Beverage Manufacturing", "Company: Kemper
Industry: Insurance", "Company: University of the Pacific
Industry: Higher Education", "Company: Fermilab
Industry: Research Services", "Company: Univar Solutions
Industry: Chemical Manufacturing", "Company: Duane Morris LLP
Industry: Law Practice", "Company: The Port Authority of New York & New Jersey
Industry: Government Administration", "Company: Associated Bank
Industry: Financial Services", "Company: Konami Digital Entertainment
Industry: Entertainment Providers", "Company: Infoblox
Industry: Computer and Network Security", "Company: Penn Mutual
Industry: Insurance", "Company: University of Vermont
Industry: Higher Education", "Company: athenahealth
Industry: IT Services and IT Consulting", "Company: Info-Tech Research Group
Industry: IT Services and IT Consulting", "Company: Sectra
Industry: IT Services and IT Consulting", "Company: City of Fort Worth
Industry: Government Administration", "Company: Bill & Melinda Gates Foundation
Industry: Non-profit Organizations", "Company: City of Atlanta
Industry: Government Administration", "Company: designory
Industry: Advertising Services", "Company: The Bolton Group
Industry: Staffing and Recruiting", "Company: Digitas Health
Industry: Advertising Services", "Company: TruTeam
Industry: Construction", "Company: Prologis
Industry: Real Estate", "Company: Plante Moran
Industry: Accounting", "Company: UChicago Medicine
Industry: Hospitals and Health Care", "Company: Cboe Global Markets
Industry: Financial Services", "Company: City and County of Denver
Industry: Government Administration", "Company: GP Strategies Corporation
Industry: Business Consulting and Services", "Company: Ghirardelli Chocolate Company
Industry: Manufacturing", "Company: State of Iowa - Executive Branch
Industry: Government Administration", "Company: Des Moines Public Schools
Industry: Education Administration Programs", "Company: ARA
Industry: Defense and Space Manufacturing", "Company: The Rockefeller University
Industry: Research Services", "Company: TSMC
Industry: Semiconductor Manufacturing", "Company: Imerys
Industry: Mining", "Company: National Hockey League (NHL)
Industry: Spectator Sports", "Company: Polaris Inc.
Industry: Manufacturing", "Company: California State University, Long Beach
Industry: Higher Education", "Company: FORVIA HELLA
Industry: Motor Vehicle Manufacturing", "Company: Western & Southern Financial Group
Industry: Financial Services", "Company: Echo Global Logistics
Industry: Truck Transportation", "Company: Greenspun Media Group
Industry: Book and Periodical Publishing", "Company: Consumer Reports
Industry: Software Development", "Company: Henry Ford Health
Industry: Hospitals and Health Care", "Company: Premier Health Partners
Industry: Hospitals and Health Care", "Company: The Mount Sinai Hospital
Industry: Hospitals and Health Care", "Company: SHI International Corp.
Industry: IT Services and IT Consulting", "Company: Newmark
Industry: Real Estate", "Company: Nuveen, a TIAA company
Industry: Financial Services", "Company: Macmillan
Industry: Book and Periodical Publishing", "Company: Clark County School District
Industry: Education Administration Programs", "Company: U.S. Chamber of Commerce
Industry: Government Relations Services", "Company: Nilfisk
Industry: Machinery Manufacturing", "Company: Proforma
Industry: Advertising Services", "Company: Belden Inc.
Industry: Manufacturing", "Company: Southwest Research Institute
Industry: Research Services", "Company: FlightSafety International
Industry: Airlines and Aviation", "Company: Laerdal Medical
Industry: Medical Equipment Manufacturing", "Company: Airgas
Industry: Chemical Manufacturing", "Company: Florida Atlantic University
Industry: Higher Education", "Company: World Wide Technology
Industry: IT Services and IT Consulting", "Company: Covestro
Industry: Chemical Manufacturing", "Company: Shaw Industries
Industry: Textile Manufacturing", "Company: Brenntag
Industry: Chemical Manufacturing", "Company: Advantage Solutions
Industry: Advertising Services", "Company: Universal Technical Institute, Inc.
Industry: Higher Education", "Company: Porsche Cars North America
Industry: Motor Vehicle Manufacturing", "Company: TรœV Rheinland North America
Industry: Public Safety", "Company: University of Missouri-Kansas City
Industry: Higher Education", "Company: H.B. Fuller
Industry: Chemical Manufacturing", "Company: SES Satellites
Industry: Telecommunications", "Company: GAF
Industry: Wholesale Building Materials", "Company: The University of Southern Mississippi
Industry: Higher Education", "Company: Advance Auto Parts
Industry: Retail", "Company: Bright Horizons
Industry: Education Administration Programs", "Company: King & Wood Mallesons
Industry: Law Practice", "Company: Indiana University Health
Industry: Hospitals and Health Care", "Company: PACSUN
Industry: Retail", "Company: Ropes & Gray LLP
Industry: Law Practice", "Company: Netsmart
Industry: Software Development", "Company: American Water
Industry: Utilities", "Company: Big Lots
Industry: Retail", "Company: Fairview Health Services
Industry: Hospitals and Health Care", "Company: Garmin
Industry: Computers and Electronics Manufacturing", "Company: Lord, Abbett & Co. LLC
Industry: Financial Services", "Company: Sheppard Mullin Richter & Hampton LLP
Industry: Law Practice", "Company: CareFirst BlueCross BlueShield
Industry: Insurance", "Company: Symetra
Industry: Insurance", "Company: National Journal
Industry: Research Services", "Company: Medical College of Wisconsin
Industry: Higher Education", "Company: Brainlab
Industry: Medical Equipment Manufacturing", "Company: Redwood Software
Industry: Software Development", "Company: University of Maryland Global Campus
Industry: Higher Education", "Company: Dallas College
Industry: Higher Education", "Company: Savills North America
Industry: Real Estate", "Company: VSP Vision Care
Industry: Insurance", "Company: Medline Industries, LP
Industry: Medical Equipment Manufacturing", "Company: Old Dominion University
Industry: Higher Education", "Company: Genesis10
Industry: IT Services and IT Consulting", "Company: Donaldson
Industry: Industrial Machinery Manufacturing", "Company: EPCOR
Industry: Utilities", "Company: J. Paul Getty Trust
Industry: Museums, Historical Sites, and Zoos", "Company: Symrise AG
Industry: Food and Beverage Manufacturing", "Company: TriNet
Industry: Human Resources Services", "Company: Braskem
Industry: Plastics Manufacturing", "Company: The Venetian Resort Las Vegas
Industry: Gambling Facilities and Casinos", "Company: Novelis
Industry: Manufacturing", "Company: Perkins&Will
Industry: Architecture and Planning", "Company: Belk
Industry: Retail", "Company: Tyler Technologies
Industry: Software Development", "Company: VHB
Industry: Civil Engineering", "Company: EY-Parthenon
Industry: Business Consulting and Services", "Company: Yara International
Industry: Farming", "Company: Simon-Kucher
Industry: Business Consulting and Services", "Company: Intuitive
Industry: Medical Equipment Manufacturing", "Company: Miratech
Industry: IT Services and IT Consulting", "Company: Peraton
Industry: IT Services and IT Consulting", "Company: Neurocrine Biosciences
Industry: Biotechnology Research", "Company: Element Fleet Management
Industry: Financial Services", "Company: Amica Insurance
Industry: Insurance", "Company: Kiewit
Industry: Construction", "Company: William & Mary
Industry: Higher Education", "Company: Guidewire Software
Industry: Software Development", "Company: Miami-Dade County Public Schools
Industry: Education Administration Programs", "Company: Trintech
Industry: Software Development", "Company: Ameren
Industry: Utilities", "Company: Benjamin Moore
Industry: Manufacturing", "Company: Design Within Reach
Industry: Furniture and Home Furnishings Manufacturing", "Company: ITW
Industry: Machinery Manufacturing", "Company: Liberty University
Industry: Higher Education", "Company: Solix Technologies, Inc.
Industry: Software Development", "Company: U.S. Office of Personnel Management (OPM)
Industry: Government Administration", "Company: VNS Health
Industry: Hospitals and Health Care", "Company: Hill's Pet Nutrition
Industry: Manufacturing", "Company: X-Rite
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Commonwealth Financial Network
Industry: Financial Services", "Company: Centene Corporation
Industry: Hospitals and Health Care", "Company: VSE Corporation
Industry: Truck Transportation", "Company: The Exchange
Industry: Retail", "Company: Novant Health
Industry: Hospitals and Health Care", "Company: Pella Corporation
Industry: Wholesale Building Materials", "Company: Babcock & Wilcox
Industry: Renewable Energy Semiconductor Manufacturing", "Company: Houston Chronicle
Industry: Newspaper Publishing", "Company: Howard Hughes Medical Institute
Industry: Research Services", "Company: Kimball International
Industry: Furniture and Home Furnishings Manufacturing", "Company: Kimley-Horn
Industry: Civil Engineering", "Company: Ansell
Industry: Manufacturing", "Company: The Metropolitan Museum of Art
Industry: Museums, Historical Sites, and Zoos", "Company: Kennesaw State University
Industry: Higher Education", "Company: The City of San Diego
Industry: Government Administration", "Company: Mercury Insurance
Industry: Insurance", "Company: Dewberry
Industry: IT Services and IT Consulting", "Company: Direct Supply
Industry: Hospitals and Health Care", "Company: Eastridge Workforce Solutions
Industry: Staffing and Recruiting", "Company: Wood
Industry: IT Services and IT Consulting", "Company: Iridium
Industry: Telecommunications", "Company: Crate and Barrel
Industry: Retail", "Company: Aveda
Industry: Personal Care Product Manufacturing", "Company: Brembo
Industry: Motor Vehicle Manufacturing", "Company: Broadspire
Industry: Insurance", "Company: Levy Restaurants
Industry: Food and Beverage Services", "Company: Rakuten Advertising
Industry: Software Development", "Company: Mintel
Industry: Market Research", "Company: Arkema
Industry: Chemical Manufacturing", "Company: Eastern Michigan University
Industry: Higher Education", "Company: Siegel+Gale
Industry: Advertising Services", "Company: Wright State University
Industry: Higher Education", "Company: Bollorรฉ Logistics
Industry: Truck Transportation", "Company: Vicor Corporation
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: California State University, Chico
Industry: Higher Education", "Company: RealPage, Inc.
Industry: Software Development", "Company: Protective Life
Industry: Insurance", "Company: Art Institute of Chicago
Industry: Museums, Historical Sites, and Zoos", "Company: Lexar
Industry: Computers and Electronics Manufacturing", "Company: Milestone Systems
Industry: Software Development", "Company: ORIX Corporation USA
Industry: Financial Services", "Company: Chevron Phillips Chemical Company
Industry: Chemical Manufacturing", "Company: Hempel A/S
Industry: Chemical Manufacturing", "Company: HUB International
Industry: Insurance", "Company: AMN Healthcare
Industry: Staffing and Recruiting", "Company: Belcan
Industry: IT Services and IT Consulting", "Company: Children's Health
Industry: Hospitals and Health Care", "Company: DSA
Industry: IT Services and IT Consulting", "Company: Hyland
Industry: Software Development", "Company: Momentive
Industry: Chemical Manufacturing", "Company: Eversource Energy
Industry: Utilities", "Company: StubHub
Industry: Software Development", "Company: Arch Insurance Group Inc.
Industry: Insurance", "Company: Giant Eagle, Inc.
Industry: Retail", "Company: Medica
Industry: Insurance", "Company: PPL Corporation
Industry: Utilities", "Company: Uponor
Industry: Wholesale Building Materials", "Company: Coinstar
Industry: Retail", "Company: CSC
Industry: Financial Services", "Company: Caleres, Inc.
Industry: Retail Apparel and Fashion", "Company: Groupe Clarins
Industry: Personal Care Product Manufacturing", "Company: Rocket Software
Industry: IT Services and IT Consulting", "Company: Alkermes
Industry: Biotechnology Research", "Company: Bracco
Industry: Pharmaceutical Manufacturing", "Company: Brooks Brothers
Industry: Retail", "Company: Famous Footwear
Industry: Retail", "Company: Creighton University
Industry: Higher Education", "Company: Qlik
Industry: Software Development", "Company: Baker Tilly US
Industry: Accounting", "Company: Ivy Tech Community College
Industry: Higher Education", "Company: NOVA Chemicals
Industry: Chemical Manufacturing", "Company: Lexicon Pharmaceuticals, Inc.
Industry: Pharmaceutical Manufacturing", "Company: PNM Resources
Industry: Utilities", "Company: Stifel Financial Corp.
Industry: Financial Services", "Company: Videojet Technologies
Industry: Printing Services", "Company: DLC
Industry: Business Consulting and Services", "Company: HNTB
Industry: Civil Engineering", "Company: GHD
Industry: Civil Engineering", "Company: JDRF International
Industry: Fundraising", "Company: Konecranes
Industry: Machinery Manufacturing", "Company: Pep Boys
Industry: Motor Vehicle Manufacturing", "Company: Subaru of America
Industry: Motor Vehicle Manufacturing", "Company: QinetiQ US
Industry: Defense and Space Manufacturing", "Company: IONOS
Industry: IT Services and IT Consulting", "Company: Sherpa | Recruiting, Staffing & Consulting
Industry: Staffing and Recruiting", "Company: New York Institute of Technology
Industry: Higher Education", "Company: Lamar Advertising Company
Industry: Advertising Services", "Company: Cable ONE
Industry: Telecommunications", "Company: LCRA
Industry: Utilities", "Company: Accuray
Industry: Medical Equipment Manufacturing", "Company: BankUnited
Industry: Banking", "Company: Nordson Corporation
Industry: Machinery Manufacturing", "Company: Giorgio Armani
Industry: Retail Apparel and Fashion", "Company: Minuteman Press
Industry: Printing Services", "Company: Swisslog
Industry: Truck Transportation", "Company: Tecan
Industry: Biotechnology Research", "Company: Shook, Hardy & Bacon L.L.P.
Industry: Law Practice", "Company: Webster Bank
Industry: Banking", "Company: CooperVision
Industry: Medical Equipment Manufacturing", "Company: Games Workshop Ltd
Industry: Retail", "Company: BRP
Industry: Manufacturing", "Company: BART
Industry: Truck Transportation", "Company: Gilbane Building Company
Industry: Construction", "Company: Mace
Industry: Construction", "Company: CARFAX
Industry: Software Development", "Company: Genuine Parts Company
Industry: Motor Vehicle Manufacturing", "Company: New York City Police Department
Industry: Law Enforcement", "Company: NBCUniversal Telemundo Enterprises
Industry: Broadcast Media Production and Distribution", "Company: BayCare Health System
Industry: Hospitals and Health Care", "Company: Meta
Industry: Software Development", "Company: Fragomen
Industry: Law Practice", "Company: Everi Holdings Inc.
Industry: Gambling Facilities and Casinos", "Company: Securian Financial
Industry: Financial Services", "Company: ESR
Industry: IT Services and IT Consulting", "Company: Vulcan Materials Company
Industry: Mining", "Company: American Psychological Association
Industry: Non-profit Organizations", "Company: Paradise Valley Hospital
Industry: Hospitals and Health Care", "Company: CarsDirect.com
Industry: Motor Vehicle Manufacturing", "Company: Promega Corporation
Industry: Biotechnology Research", "Company: Steptoe LLP
Industry: Law Practice", "Company: (USTA) United States Tennis Association
Industry: Spectator Sports", "Company: BAI
Industry: Financial Services", "Company: Chick-fil-A Corporate Support Center
Industry: Restaurants", "Company: CHRISTUS Health
Industry: Hospitals and Health Care", "Company: Rent-A-Center
Industry: Retail", "Company: SNI Financial
Industry: Staffing and Recruiting", "Company: Cooper Standard
Industry: Motor Vehicle Manufacturing", "Company: eInfochips (An Arrow Company)
Industry: IT Services and IT Consulting", "Company: Fresenius Kabi USA
Industry: Pharmaceutical Manufacturing", "Company: Converse
Industry: Retail Apparel and Fashion", "Company: NORC at the University of Chicago
Industry: Research Services", "Company: ACT
Industry: Education Administration Programs", "Company: Butler Aerospace & Defense
Industry: Staffing and Recruiting", "Company: City of Phoenix
Industry: Government Administration", "Company: David's Bridal
Industry: Retail", "Company: Quinnox
Industry: IT Services and IT Consulting", "Company: Crowell & Moring
Industry: Law Practice", "Company: The Wonderful Company
Industry: Food and Beverage Manufacturing", "Company: POM Wonderful
Industry: Food and Beverage Manufacturing", "Company: FIJI Water
Industry: Beverage Manufacturing", "Company: Semtech
Industry: Software Development", "Company: U.S. Xpress, Inc.
Industry: Truck Transportation", "Company: Brookfield Properties
Industry: Real Estate", "Company: GKN Aerospace
Industry: Aviation and Aerospace Component Manufacturing", "Company: Alzheimer's Associationยฎ
Industry: Non-profit Organizations", "Company: Swagelok
Industry: Industrial Machinery Manufacturing", "Company: The J.M. Smucker Co.
Industry: Manufacturing", "Company: Turner & Townsend
Industry: Construction", "Company: EverBank
Industry: Banking", "Company: Heartland
Industry: Financial Services", "Company: Insight Global
Industry: Staffing and Recruiting", "Company: Liebherr Group
Industry: Industrial Machinery Manufacturing", "Company: Pinnacle Group, Inc.
Industry: IT Services and IT Consulting", "Company: Starkey Hearing
Industry: Medical Equipment Manufacturing", "Company: Swissport
Industry: Airlines and Aviation", "Company: University of Mississippi
Industry: Higher Education", "Company: Orlando Health
Industry: Hospitals and Health Care", "Company: Terminix
Industry: Consumer Services", "Company: Westat
Industry: Research Services", "Company: ESCO Group LLC
Industry: Mining", "Company: Infinite Computer Solutions
Industry: IT Services and IT Consulting", "Company: KSB Company
Industry: Industrial Machinery Manufacturing", "Company: New York Post
Industry: Media Production", "Company: Nova Ltd.
Industry: Semiconductor Manufacturing", "Company: Tom James Company
Industry: Retail Apparel and Fashion", "Company: Berry Global, Inc.
Industry: Plastics Manufacturing", "Company: Douglas County
Industry: Government Administration", "Company: Kinder Morgan, Inc.
Industry: Oil and Gas", "Company: HSB - Hartford Steam Boiler
Industry: Insurance", "Company: Venable LLP
Industry: Law Practice", "Company: Environmental Defense Fund
Industry: Non-profit Organizations", "Company: FUJIFILM Healthcare Americas Corporation
Industry: Medical Equipment Manufacturing", "Company: Hill International, Inc.
Industry: Construction", "Company: Matson, Inc.
Industry: Transportation, Logistics, Supply Chain and Storag", "Company: Matson Logistics
Industry: Truck Transportation", "Company: Shiseido
Industry: Personal Care Product Manufacturing", "Company: Travis County
Industry: Government Administration", "Company: Vaco
Industry: Staffing and Recruiting", "Company: Burlington Stores, Inc.
Industry: Retail", "Company: Tarkett
Industry: Wholesale Building Materials", "Company: UAMS - University of Arkansas for Medical Sciences
Industry: Hospitals and Health Care", "Company: Yazaki North America
Industry: Motor Vehicle Manufacturing", "Company: Girl Scouts of the USA
Industry: Civic and Social Organizations", "Company: Graco
Industry: Machinery Manufacturing", "Company: Hillel International
Industry: Non-profit Organizations", "Company: Leggett & Platt
Industry: Furniture and Home Furnishings Manufacturing", "Company: SHRM
Industry: Human Resources Services", "Company: National Renewable Energy Laboratory
Industry: Research Services", "Company: Prysmian
Industry: Manufacturing", "Company: Clark Construction Group
Industry: Construction", "Company: Marlabs LLC
Industry: IT Services and IT Consulting", "Company: Children's National Hospital
Industry: Hospitals and Health Care", "Company: ANDRITZ
Industry: Machinery Manufacturing", "Company: Austin Community College
Industry: Higher Education", "Company: Hologic, Inc.
Industry: Medical Equipment Manufacturing", "Company: XTRA Lease LLC
Industry: Truck Transportation", "Company: General Atomics
Industry: Defense and Space Manufacturing", "Company: Ingenio
Industry: Software Development", "Company: Janney Montgomery Scott LLC
Industry: Financial Services", "Company: NCDOT
Industry: Truck Transportation", "Company: Almac Group
Industry: Pharmaceutical Manufacturing", "Company: Citi
Industry: Financial Services", "Company: Siemens Gamesa
Industry: Renewable Energy Semiconductor Manufacturing", "Company: PGA TOUR
Industry: Spectator Sports", "Company: MGIC
Industry: Insurance", "Company: Onward Technologies Limited
Industry: Software Development", "Company: SageNet
Industry: Telecommunications", "Company: Wood Mackenzie
Industry: Information Services", "Company: Arlington County Government
Industry: Government Administration", "Company: O.C. Tanner
Industry: Human Resources Services", "Company: PVH Corp.
Industry: Retail Apparel and Fashion", "Company: Bartech Staffing
Industry: Staffing and Recruiting", "Company: Woodside Energy
Industry: Oil and Gas", "Company: BDS Connected Solutions, LLC.
Industry: Advertising Services", "Company: NFP
Industry: Insurance", "Company: Navy Federal Credit Union
Industry: Financial Services", "Company: TriWest Healthcare Alliance
Industry: Hospitals and Health Care", "Company: AAAS
Industry: Non-profit Organizations", "Company: Hormel Foods
Industry: Manufacturing", "Company: Mainline Information Systems
Industry: IT Services and IT Consulting", "Company: Midcontinent Independent System Operator (MISO)
Industry: Utilities", "Company: WEX
Industry: Software Development", "Company: Barings
Industry: Financial Services", "Company: BioMarin Pharmaceutical Inc.
Industry: Biotechnology Research", "Company: C&S Wholesale Grocers
Industry: Wholesale", "Company: Open Systems Technologies
Industry: Staffing and Recruiting", "Company: SolomonEdwards
Industry: Business Consulting and Services", "Company: AAA-The Auto Club Group
Industry: Insurance", "Company: Institute for Defense Analyses
Industry: Defense and Space Manufacturing", "Company: MAC Cosmetics
Industry: Personal Care Product Manufacturing", "Company: Markel
Industry: Insurance", "Company: Proofpoint
Industry: Computer and Network Security", "Company: Rich Products Corporation
Industry: Food and Beverage Manufacturing", "Company: Combined, a Chubb Company
Industry: Insurance", "Company: Leonardo
Industry: Defense and Space Manufacturing", "Company: Freedom Mortgage
Industry: Financial Services", "Company: Oceaneering
Industry: Oil and Gas", "Company: Trinity College-Hartford
Industry: Higher Education", "Company: Tennant Company
Industry: Machinery Manufacturing", "Company: Wesco
Industry: Wholesale", "Company: OneAmerica Financial
Industry: Financial Services", "Company: Strayer University
Industry: Higher Education", "Company: Zilliant
Industry: Software Development", "Company: Medical Mutual
Industry: Insurance", "Company: Atlantic Health System
Industry: Hospitals and Health Care", "Company: Baptist Health
Industry: Hospitals and Health Care", "Company: Trader Joe's
Industry: Retail", "Company: Avature
Industry: Software Development", "Company: Bank of Hawaii
Industry: Banking", "Company: Boise State University
Industry: Higher Education", "Company: Broadridge
Industry: Financial Services", "Company: Keypath Education
Industry: Higher Education", "Company: Arby's
Industry: Restaurants", "Company: Barrick Gold Corporation
Industry: Mining", "Company: Centric Consulting
Industry: Business Consulting and Services", "Company: ITR Group
Industry: IT Services and IT Consulting", "Company: Main Line Health
Industry: Hospitals and Health Care", "Company: Myriad Genetics
Industry: Biotechnology Research", "Company: Boost Mobile
Industry: Telecommunications", "Company: Cambridge Health Alliance
Industry: Hospitals and Health Care", "Company: Novanta Inc.
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Virginia Mason Franciscan Health
Industry: Hospitals and Health Care", "Company: Wilson Elser
Industry: Law Practice", "Company: Epiq
Industry: Law Practice", "Company: Griffith Foods
Industry: Food and Beverage Manufacturing", "Company: Buchanan Ingersoll & Rooney PC
Industry: Law Practice", "Company: Sg2
Industry: Hospitals and Health Care", "Company: UT Health San Antonio
Industry: Higher Education", "Company: Medidata Solutions
Industry: Software Development", "Company: Park Nicollet Health Services
Industry: Hospitals and Health Care", "Company: Ocean Spray Cranberries
Industry: Food and Beverage Services", "Company: Pratt Institute
Industry: Higher Education", "Company: BENTELER Group
Industry: Motor Vehicle Manufacturing", "Company: Towson University
Industry: Higher Education", "Company: Ionis Pharmaceuticals, Inc.
Industry: Biotechnology Research", "Company: Paladin Consulting
Industry: Staffing and Recruiting", "Company: STV
Industry: Civil Engineering", "Company: OpenTable
Industry: Hospitality", "Company: Republican National Committee
Industry: Political Organizations", "Company: Safelite
Industry: Retail", "Company: Tradeweb
Industry: Financial Services", "Company: Advantage Resourcing
Industry: Staffing and Recruiting", "Company: Bon Secours
Industry: Hospitals and Health Care", "Company: Denver Public Schools
Industry: Primary and Secondary Education", "Company: Farm Bureau Financial Services
Industry: Insurance", "Company: Audible
Industry: Software Development", "Company: University of Missouri-Saint Louis
Industry: Higher Education", "Company: La-Z-Boy Incorporated
Industry: Furniture and Home Furnishings Manufacturing", "Company: MedImpact Healthcare Systems, Inc.
Industry: Pharmaceutical Manufacturing", "Company: Day & Zimmermann
Industry: Construction", "Company: Graphic Packaging International, LLC
Industry: Packaging and Containers Manufacturing", "Company: Idaho National Laboratory
Industry: Research Services", "Company: Rose International
Industry: Staffing and Recruiting", "Company: National Federation of Independent Business (NFIB)
Industry: Non-profit Organizations", "Company: Culligan International
Industry: Manufacturing", "Company: Sentry
Industry: Insurance", "Company: SICK Sensor Intelligence
Industry: Automation Machinery Manufacturing", "Company: Trapeze Group
Industry: Software Development", "Company: University of Richmond
Industry: Higher Education", "Company: Welch's
Industry: Manufacturing", "Company: Miami Dade College
Industry: Higher Education", "Company: Americold Logistics, LLC.
Industry: Truck Transportation", "Company: Atlas Air
Industry: Airlines and Aviation", "Company: Circle K
Industry: Retail", "Company: EQT Corporation
Industry: Oil and Gas", "Company: Mimeo
Industry: Printing Services", "Company: FCS Software Solutions Ltd
Industry: IT Services and IT Consulting", "Company: Leica Microsystems
Industry: Medical Equipment Manufacturing", "Company: Leviton
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Conservation International
Industry: Non-profit Organizations", "Company: Cracker Barrel
Industry: Restaurants", "Company: DPR Construction
Industry: Construction", "Company: PAR Technology
Industry: IT Services and IT Consulting", "Company: UNOPS
Industry: Non-profit Organizations", "Company: Granite Construction
Industry: Construction", "Company: General Dynamics Electric Boat
Industry: Defense and Space Manufacturing", "Company: Markem-Imaje
Industry: Automation Machinery Manufacturing", "Company: PDF Solutions
Industry: Software Development", "Company: Pilgrim's
Industry: Food and Beverage Manufacturing", "Company: Uline
Industry: Wholesale", "Company: Yardi
Industry: Software Development", "Company: ASQ - World Headquarters
Industry: Non-profit Organizations", "Company: CompHealth
Industry: Staffing and Recruiting", "Company: Sensient Technologies Corporation
Industry: Chemical Manufacturing", "Company: Windstream
Industry: Telecommunications", "Company: Food Lion
Industry: Retail", "Company: Brookhaven National Laboratory
Industry: Research Services", "Company: Copyright Clearance Center (CCC)
Industry: Information Services", "Company: Crum & Forster
Industry: Insurance", "Company: UST
Industry: IT Services and IT Consulting", "Company: Detroit Medical Center
Industry: Hospitals and Health Care", "Company: Children's Hospital of Michigan
Industry: Hospitals and Health Care", "Company: Exclusive Resorts
Industry: Hospitality", "Company: Federal Bureau of Prisons - Career Connections
Industry: Law Enforcement", "Company: Montclair State University
Industry: Higher Education", "Company: Altec
Industry: Machinery Manufacturing", "Company: Scooter's Coffee
Industry: Food and Beverage Services", "Company: Holland & Hart LLP
Industry: Law Practice", "Company: Sargent & Lundy
Industry: IT Services and IT Consulting", "Company: Sierra Nevada Corporation
Industry: Defense and Space Manufacturing", "Company: Cabela's
Industry: Retail", "Company: Burns & McDonnell
Industry: Construction", "Company: ChristianaCare
Industry: Hospitals and Health Care", "Company: 2K
Industry: Computer Games", "Company: Viking
Industry: Travel Arrangements", "Company: HealthFitness
Industry: Wellness and Fitness Services", "Company: Hexcel Corporation
Industry: Aviation and Aerospace Component Manufacturing", "Company: HMSHost
Industry: Food and Beverage Services", "Company: IREX
Industry: Non-profit Organizations", "Company: Pernod Ricard
Industry: Beverage Manufacturing", "Company: CuraScript SD by Evernorth
Industry: Pharmaceutical Manufacturing", "Company: Genmab
Industry: Biotechnology Research", "Company: Loomis, Sayles & Company
Industry: Financial Services", "Company: Boys & Girls Clubs of America
Industry: Non-profit Organizations", "Company: SMX
Industry: IT Services and IT Consulting", "Company: Hendrickson
Industry: Truck Transportation", "Company: Rittal North America LLC
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Sinclair Inc.
Industry: Broadcast Media Production and Distribution", "Company: Smith Hanley Associates
Industry: Staffing and Recruiting", "Company: eHealth, Inc.
Industry: Insurance", "Company: Mercy Health
Industry: Hospitals and Health Care", "Company: MultiCare Health System
Industry: Hospitals and Health Care", "Company: New Resources Consulting
Industry: IT Services and IT Consulting", "Company: Orange County Government
Industry: Government Administration", "Company: Biotage
Industry: Biotechnology Research", "Company: Harris County
Industry: Government Administration", "Company: PENN Entertainment, Inc
Industry: Gambling Facilities and Casinos", "Company: HurixDigital
Industry: IT Services and IT Consulting", "Company: Mindteck
Industry: IT Services and IT Consulting", "Company: Aggreko
Industry: Utilities", "Company: Aston Carter
Industry: Staffing and Recruiting", "Company: Beam Suntory
Industry: Beverage Manufacturing", "Company: Constant Contact
Industry: Advertising Services", "Company: Acronis
Industry: Software Development", "Company: Adventist HealthCare
Industry: Hospitals and Health Care", "Company: Management Sciences for Health
Industry: Non-profit Organizations", "Company: City of Indianapolis
Industry: Government Administration", "Company: Empire Today
Industry: Retail", "Company: Kao Corporation
Industry: Personal Care Product Manufacturing", "Company: Modine Manufacturing Company
Industry: Manufacturing", "Company: Optiver
Industry: Financial Services", "Company: Frost
Industry: Financial Services", "Company: Hiscox
Industry: Insurance", "Company: Nexon America
Industry: Computer Games", "Company: Parkland Health
Industry: Hospitals and Health Care", "Company: Accruent
Industry: Software Development", "Company: TransPerfect
Industry: Translation and Localization", "Company: Systems Planning & Analysis
Industry: Defense and Space Manufacturing", "Company: Albany International Corp.
Industry: Textile Manufacturing", "Company: Conair LLC
Industry: Manufacturing", "Company: Integra LifeSciences
Industry: Medical Equipment Manufacturing", "Company: Ledgent
Industry: Staffing and Recruiting", "Company: McLane Company, Inc.
Industry: Truck Transportation", "Company: St. Joseph Health
Industry: Hospitals and Health Care", "Company: Zimmerman Advertising
Industry: Advertising Services", "Company: HKS, Inc.
Industry: Architecture and Planning", "Company: Skechers
Industry: Retail", "Company: ELEKS
Industry: IT Services and IT Consulting", "Company: Cambrex
Industry: Pharmaceutical Manufacturing", "Company: Children's Minnesota
Industry: Hospitals and Health Care", "Company: Constellation Brands
Industry: Food and Beverage Services", "Company: Opportunity International
Industry: Non-profit Organizations", "Company: Regeneron
Industry: Biotechnology Research", "Company: Leadership Institute
Industry: Political Organizations", "Company: ValueLabs
Industry: IT Services and IT Consulting", "Company: Contra Costa County
Industry: Government Administration", "Company: Devereux Advanced Behavioral Health
Industry: Mental Health Care", "Company: Gordon Rees Scully Mansukhani, LLP
Industry: Law Practice", "Company: Harris Computer
Industry: Software Development", "Company: Greif
Industry: Packaging and Containers Manufacturing", "Company: CohnReznick LLP
Industry: Accounting", "Company: Excelacom
Industry: IT Services and IT Consulting", "Company: World Learning
Industry: Non-profit Organizations", "Company: Signet Jewelers
Industry: Retail", "Company: Brose Group
Industry: Motor Vehicle Manufacturing", "Company: Jackson Lewis P.C.
Industry: Law Practice", "Company: MarketAxess
Industry: Financial Services", "Company: Oakland University
Industry: Higher Education", "Company: Portland General Electric
Industry: Utilities", "Company: Chamberlain Group
Industry: Software Development", "Company: TranSystems
Industry: Civil Engineering", "Company: Trigyn Technologies
Industry: IT Services and IT Consulting", "Company: ArisGlobal
Industry: Software Development", "Company: CoBank
Industry: Banking", "Company: Magna International
Industry: Motor Vehicle Manufacturing", "Company: Stepan Company
Industry: Chemical Manufacturing", "Company: ZOLL Medical Corporation
Industry: Medical Equipment Manufacturing", "Company: WiseTech Global
Industry: Software Development", "Company: ABF Freight
Industry: Truck Transportation", "Company: Spirit AeroSystems
Industry: Aviation and Aerospace Component Manufacturing", "Company: Williams College
Industry: Higher Education", "Company: PING
Industry: Retail", "Company: Prime Therapeutics
Industry: Insurance", "Company: United States Olympic & Paralympic Committee
Industry: Spectator Sports", "Company: Burns & Levinson LLP
Industry: Law Practice", "Company: Community Health Network
Industry: Hospitals and Health Care", "Company: Eliassen Group
Industry: Business Consulting and Services", "Company: Freudenberg Sealing Technologies
Industry: Manufacturing", "Company: Lesley University
Industry: Higher Education", "Company: Marathon Petroleum Corporation
Industry: Oil and Gas", "Company: Ballard Spahr LLP
Industry: Law Practice", "Company: Bluegreen Vacations
Industry: Hospitality", "Company: Zegna
Industry: Retail Luxury Goods and Jewelry", "Company: Hach
Industry: Environmental Services", "Company: International Atomic Energy Agency (IAEA)
Industry: International Affairs", "Company: Apex IT
Industry: IT Services and IT Consulting", "Company: Mortenson
Industry: Construction", "Company: Mozilla
Industry: Software Development", "Company: expand group
Industry: Staffing and Recruiting", "Company: BECU
Industry: Banking", "Company: Wheels, Inc.
Industry: Financial Services", "Company: Zillow
Industry: Real Estate", "Company: Expro
Industry: Oil and Gas", "Company: Builders FirstSource
Industry: Wholesale Building Materials", "Company: CES
Industry: IT Services and IT Consulting", "Company: Dematic
Industry: Truck Transportation", "Company: Western Governors University
Industry: Higher Education", "Company: LensCrafters
Industry: Retail Apparel and Fashion", "Company: The Mars Agency
Industry: Advertising Services", "Company: Prosum
Industry: Staffing and Recruiting", "Company: RadNet
Industry: Hospitals and Health Care", "Company: SANS Institute
Industry: Computer and Network Security", "Company: Volvo Financial Services
Industry: Financial Services", "Company: Under Armour
Industry: Retail Apparel and Fashion", "Company: Softworld, a Kelly Company
Industry: Staffing and Recruiting", "Company: AvalonBay Communities
Industry: Real Estate", "Company: Aรฉropostale
Industry: Retail", "Company: Carters Inc.
Industry: Retail Apparel and Fashion", "Company: Dallas Fort Worth International Airport (DFW)
Industry: Airlines and Aviation", "Company: CJ Logistics America
Industry: Truck Transportation", "Company: Wiley Rein LLP
Industry: Law Practice", "Company: VisitBritain
Industry: Travel Arrangements", "Company: Lhoist
Industry: Mining", "Company: Printpack
Industry: Packaging and Containers Manufacturing", "Company: Academy Sports + Outdoors
Industry: Retail", "Company: Cascades
Industry: Packaging and Containers Manufacturing", "Company: Staff Management | SMX
Industry: Staffing and Recruiting", "Company: Wildlife Conservation Society
Industry: Non-profit Organizations", "Company: Solomon Page
Industry: Staffing and Recruiting", "Company: RWJBarnabas Health
Industry: Hospitals and Health Care", "Company: Guaranteed Rate
Industry: Financial Services", "Company: KARL STORZ United States
Industry: Medical Equipment Manufacturing", "Company: GIA (Gemological Institute of America)
Industry: Non-profit Organizations", "Company: Papa Johns
Industry: Restaurants", "Company: Clean Harbors
Industry: Environmental Services", "Company: Denver Health
Industry: Hospitals and Health Care", "Company: CSI
Industry: IT Services and IT Consulting", "Company: Transurban
Industry: Truck Transportation", "Company: American Modern Insurance Group
Industry: Insurance", "Company: Perry Ellis International
Industry: Retail Apparel and Fashion", "Company: P.F. Chang's
Industry: Restaurants", "Company: East West Bank
Industry: Banking", "Company: Newegg
Industry: Retail", "Company: Armanino LLP
Industry: Accounting", "Company: AVEVA
Industry: Software Development", "Company: Susan G. Komen
Industry: Non-profit Organizations", "Company: Zeno Group
Industry: Public Relations and Communications Services", "Company: EMCOR Group, Inc.
Industry: Construction", "Company: Avangrid
Industry: Utilities", "Company: Erie Insurance Group
Industry: Insurance", "Company: Tommy Bahama
Industry: Retail Apparel and Fashion", "Company: Eastern Bank
Industry: Banking", "Company: iCIMS
Industry: Software Development", "Company: Comrise
Industry: Staffing and Recruiting", "Company: McLean Hospital
Industry: Mental Health Care", "Company: Movado Group, Inc
Industry: Retail Luxury Goods and Jewelry", "Company: Jefferson Health
Industry: Hospitals and Health Care", "Company: Peterson's
Industry: Education Administration Programs", "Company: Selective Insurance
Industry: Insurance", "Company: Batesville
Industry: Manufacturing", "Company: Butler University
Industry: Higher Education", "Company: Profiles
Industry: Staffing and Recruiting", "Company: Renaissance Learning
Industry: Primary and Secondary Education", "Company: Scripps Health
Industry: Hospitals and Health Care", "Company: Tampa Electric
Industry: Utilities", "Company: Inditex
Industry: Retail", "Company: Universal Instruments Corporation
Industry: Appliances, Electrical, and Electronics Manufactur", "Company: Jockey International, Inc.
Industry: Retail", "Company: Metropolitan State University of Denver
Industry: Higher Education", "Company: Trinity Industries, Inc.
Industry: Truck Transportation", "Company: EDB
Industry: Software Development", "Company: Hannaford Supermarkets
Industry: Retail", "Company: HealthStream
Industry: Software Development", "Company: Performance Food Group
Industry: Food and Beverage Services", "Company: Baptist Health
Industry: Hospitals and Health Care", "Company: City of Palo Alto
Industry: Government Administration", "Company: Ansira
Industry: Advertising Services", "Company: RED Global
Industry: IT Services and IT Consulting", "Company: FUJIFILM Sonosite, Inc.
Industry: Medical Equipment Manufacturing", "Company: Tripadvisor
Industry: Software Development", "Company: Kimpton Hotels & Restaurants
Industry: Hospitality", "Company: Humanscale
Industry: Furniture and Home Furnishings Manufacturing", "Company: Designit
Industry: Design Services", "Company: Dimensional Fund Advisors
Industry: Financial Services", "Company: Delaware North
Industry: Hospitality", "Company: Westgate Resorts
Industry: Hospitality", "Company: Graham Packaging
Industry: Packaging and Containers Manufacturing", "Company: Innovative Systems Group
Industry: IT Services and IT Consulting", "Company: ENGIE North America Inc.
Industry: Renewable Energy Semiconductor Manufacturing", "Company: Pacific International Executive Search
Industry: Staffing and Recruiting", "Company: Formica Group North America
Industry: Wholesale Building Materials", "Company: AmeriGas
Industry: Oil and Gas", "Company: Arriva Group
Industry: Truck Transportation", "Company: Hilton Grand Vacations
Industry: Hospitality", "Company: Texas Tech University Health Sciences Center
Industry: Higher Education", "Company: JELD-WEN, Inc.
Industry: Wholesale Building Materials", "Company: Kleinfelder
Industry: Civil Engineering", "Company: Ontex
Industry: Manufacturing", "Company: Acushnet Company
Industry: Manufacturing", "Company: Ambu A/S
Industry: Medical Equipment Manufacturing", "Company: DISYS
Industry: IT Services and IT Consulting", "Company: Neudesic, an IBM Company
Industry: IT Services and IT Consulting", "Company: FreshDirect
Industry: Food and Beverage Services", "Company: Hong Kong Trade Development Council
Industry: International Trade and Development", "Company: 1-800 CONTACTS
Industry: Retail", "Company: Molina Healthcare
Industry: Hospitals and Health Care", "Company: LA Fitness
Industry: Wellness and Fitness Services", "Company: Boingo Wireless
Industry: Telecommunications", "Company: Boston Technology Corporation
Industry: IT Services and IT Consulting", "Company: Copart
Industry: Motor Vehicle Manufacturing", "Company: Choate, Hall & Stewart LLP
Industry: Law Practice", "Company: SPS Commerce
Industry: IT Services and IT Consulting", "Company: Downstate Health Sciences University
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"bordercolor": "rgb(17,17,17)", "borderwidth": 1, "tickwidth": 0 }, "ternary": { "aaxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" }, "baxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" }, "bgcolor": "rgb(17,17,17)", "caxis": { "gridcolor": "#506784", "linecolor": "#506784", "ticks": "" } }, "title": { "x": 0.05 }, "updatemenudefaults": { "bgcolor": "#506784", "borderwidth": 0 }, "xaxis": { "automargin": true, "gridcolor": "#283442", "linecolor": "#506784", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#283442", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "#283442", "linecolor": "#506784", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "#283442", "zerolinewidth": 2 } } }, "title": { "text": "๐ŸŒŒ HRHUB v2.1 - Candidate-Company Embedding Space (t-SNE)" }, "width": 1200, "xaxis": { "title": { "text": "t-SNE Dimension 1" } }, "yaxis": { "title": { "text": "t-SNE Dimension 2" } } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 10: t-SNE Visualization (Interactive Plotly)\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "from sklearn.manifold import TSNE\n", "import plotly.graph_objects as go\n", "\n", "print(\"๐ŸŒŒ GENERATING t-SNE VISUALIZATION...\")\n", "print(\"=\" * 80)\n", "\n", "# Sample for speed (full dataset takes too long)\n", "n_sample = min(2000, len(cand_vectors))\n", "sample_cands = cand_vectors[:n_sample]\n", "sample_comps = comp_vectors[:n_sample]\n", "\n", "print(f\"\\n๐Ÿ“Š Sampling:\")\n", "print(f\" Candidates: {len(sample_cands):,}\")\n", "print(f\" Companies: {len(sample_comps):,}\")\n", "\n", "# Combine\n", "all_vectors = np.vstack([sample_cands, sample_comps])\n", "labels = ['Candidate'] * len(sample_cands) + ['Company'] * len(sample_comps)\n", "\n", "print(f\"\\n๐Ÿ”„ Running t-SNE (this takes ~2-3 min)...\")\n", "\n", "tsne = TSNE(\n", " n_components=2,\n", " random_state=42,\n", " perplexity=30,\n", " n_iter=1000,\n", " verbose=1\n", ")\n", "\n", "coords_2d = tsne.fit_transform(all_vectors)\n", "\n", "print(f\"\\nโœ… t-SNE complete! Shape: {coords_2d.shape}\")\n", "\n", "# Split back\n", "cand_coords = coords_2d[:len(sample_cands)]\n", "comp_coords = coords_2d[len(sample_cands):]\n", "\n", "# Create interactive plot\n", "fig = go.Figure()\n", "\n", "# Candidates (green)\n", "fig.add_trace(go.Scatter(\n", " x=cand_coords[:, 0],\n", " y=cand_coords[:, 1],\n", " mode='markers',\n", " name='Candidates',\n", " marker=dict(\n", " size=6,\n", " color='#2ecc71',\n", " opacity=0.6,\n", " line=dict(width=0)\n", " ),\n", " text=[f\"Candidate {i}
{candidates.iloc[i].get('Category', 'N/A')}\" \n", " for i in range(len(sample_cands))],\n", " hovertemplate='%{text}'\n", "))\n", "\n", "# Companies (red)\n", "fig.add_trace(go.Scatter(\n", " x=comp_coords[:, 0],\n", " y=comp_coords[:, 1],\n", " mode='markers',\n", " name='Companies',\n", " marker=dict(\n", " size=6,\n", " color='#e74c3c',\n", " opacity=0.6,\n", " line=dict(width=0)\n", " ),\n", " text=[f\"Company: {companies_full.iloc[i].get('name', 'N/A')}
Industry: {companies_full.iloc[i].get('industries_list', 'N/A')[:50]}\" \n", " for i in range(len(sample_comps))],\n", " hovertemplate='%{text}'\n", "))\n", "\n", "fig.update_layout(\n", " title='๐ŸŒŒ HRHUB v2.1 - Candidate-Company Embedding Space (t-SNE)',\n", " xaxis_title='t-SNE Dimension 1',\n", " yaxis_title='t-SNE Dimension 2',\n", " width=1200,\n", " height=800,\n", " template='plotly_dark',\n", " hovermode='closest'\n", ")\n", "\n", "# Save HTML\n", "tsne_path = f'{Config.RESULTS_PATH}tsne_interactive.html'\n", "fig.write_html(tsne_path)\n", "\n", "print(f\"\\n๐Ÿ’พ Saved: {tsne_path}\")\n", "print(f\"\\n๐ŸŽฏ KEY INSIGHT:\")\n", "print(\" If job posting bridge works โ†’ candidates & companies should overlap!\")\n", "print(\"=\" * 80)\n", "\n", "# Show in notebook\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ•ธ๏ธ GENERATING PYVIS INTERACTIVE NETWORK...\n", "================================================================================\n", "\n", "๐Ÿ“Š Network size:\n", " Candidates: 50\n", " Companies: 100\n", " Max edges: 250 (top 5 per candidate)\n", "Warning: When cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n", "\n", "๐Ÿ”ต Adding candidate nodes...\n", "๐Ÿ”ด Adding company nodes...\n", "๐Ÿ”— Adding edges (matches)...\n", "\n", "โœ… Network built!\n", " Nodes: 150\n", " Edges: 6\n", "\n", "๐Ÿ’พ Saved: ../results/network_interactive.html\n", "\n", "๐ŸŽฏ USAGE:\n", " - Drag nodes to rearrange\n", " - Hover for details\n", " - Zoom with mouse wheel\n", " - Green = Candidates, Red = Companies\n", "================================================================================\n", "../results/network_interactive.html\n" ] }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 11: PyVis Interactive Network (Drag & Drop Graph)\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "from pyvis.network import Network\n", "import random\n", "\n", "print(\"๐Ÿ•ธ๏ธ GENERATING PYVIS INTERACTIVE NETWORK...\")\n", "print(\"=\" * 80)\n", "\n", "# Sample for visualization (too many = slow)\n", "n_candidates = min(50, len(candidates))\n", "n_companies = min(100, len(companies_full))\n", "\n", "print(f\"\\n๐Ÿ“Š Network size:\")\n", "print(f\" Candidates: {n_candidates}\")\n", "print(f\" Companies: {n_companies}\")\n", "print(f\" Max edges: {n_candidates * 5} (top 5 per candidate)\")\n", "\n", "# Initialize network\n", "net = Network(\n", " height='800px',\n", " width='100%',\n", " bgcolor='#1a1a1a',\n", " font_color='white',\n", " notebook=True\n", ")\n", "\n", "# Physics settings for nice layout\n", "net.set_options(\"\"\"\n", "{\n", " \"physics\": {\n", " \"forceAtlas2Based\": {\n", " \"gravitationalConstant\": -50,\n", " \"centralGravity\": 0.01,\n", " \"springLength\": 100,\n", " \"springConstant\": 0.08\n", " },\n", " \"maxVelocity\": 50,\n", " \"solver\": \"forceAtlas2Based\",\n", " \"timestep\": 0.35,\n", " \"stabilization\": {\"iterations\": 150}\n", " }\n", "}\n", "\"\"\")\n", "\n", "print(f\"\\n๐Ÿ”ต Adding candidate nodes...\")\n", "\n", "# Add candidate nodes (green)\n", "for i in range(n_candidates):\n", " cand = candidates.iloc[i]\n", " node_id = f\"C{i}\"\n", " \n", " skills = str(cand.get('skills', 'N/A'))[:100]\n", " category = cand.get('Category', 'Unknown')\n", " \n", " net.add_node(\n", " node_id,\n", " label=f\"Candidate {i}\",\n", " title=f\"Candidate {i}
Category: {category}
Skills: {skills}...\",\n", " color='#2ecc71',\n", " size=20,\n", " shape='dot'\n", " )\n", "\n", "print(f\"๐Ÿ”ด Adding company nodes...\")\n", "\n", "# Add company nodes (red)\n", "for i in range(n_companies):\n", " comp = companies_full.iloc[i]\n", " node_id = f\"CO{i}\"\n", " \n", " name = comp.get('name', 'Unknown')\n", " industry = str(comp.get('industries_list', 'N/A'))[:100]\n", " \n", " net.add_node(\n", " node_id,\n", " label=name[:20],\n", " title=f\"{name}
Industry: {industry}...\",\n", " color='#e74c3c',\n", " size=15,\n", " shape='dot'\n", " )\n", "\n", "print(f\"๐Ÿ”— Adding edges (matches)...\")\n", "\n", "# Add edges (top 5 matches per candidate)\n", "edge_count = 0\n", "for cand_idx in range(n_candidates):\n", " matches = find_top_matches(cand_idx, top_k=5)\n", " \n", " for comp_idx, score in matches:\n", " if comp_idx < n_companies: # Only if company in sample\n", " net.add_edge(\n", " f\"C{cand_idx}\",\n", " f\"CO{comp_idx}\",\n", " value=float(score * 10), # Thickness based on score\n", " title=f\"Match Score: {score:.3f}\",\n", " color={'color': '#95a5a6', 'opacity': 0.3}\n", " )\n", " edge_count += 1\n", "\n", "print(f\"\\nโœ… Network built!\")\n", "print(f\" Nodes: {n_candidates + n_companies}\")\n", "print(f\" Edges: {edge_count}\")\n", "\n", "# Save HTML\n", "network_path = f'{Config.RESULTS_PATH}network_interactive.html'\n", "net.save_graph(network_path)\n", "\n", "print(f\"\\n๐Ÿ’พ Saved: {network_path}\")\n", "print(f\"\\n๐ŸŽฏ USAGE:\")\n", "print(\" - Drag nodes to rearrange\")\n", "print(\" - Hover for details\")\n", "print(\" - Zoom with mouse wheel\")\n", "print(\" - Green = Candidates, Red = Companies\")\n", "print(\"=\" * 80)\n", "\n", "# Show in notebook\n", "net.show(network_path)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ“Š EVALUATION METRICS\n", "================================================================================\n", "\n", "1๏ธโƒฃ MATCH SCORE DISTRIBUTION\n", " Sample size: 500 candidates ร— 10 matches = 5000 scores\n", "\n", " Statistics:\n", " Mean: 0.5730\n", " Median: 0.5728\n", " Std: 0.0423\n", " Min: 0.4442\n", " Max: 0.7320\n", "\n", " ๐Ÿ’พ Saved: score_distribution.png\n", "\n", "2๏ธโƒฃ BILATERAL FAIRNESS RATIO\n", " Candidate โ†’ Company avg: 0.5870\n", " Company โ†’ Candidate avg: 0.4219\n", " Bilateral Fairness Ratio: 0.7188\n", " ๐ŸŸก Acceptable (>0.70)\n", "\n", "3๏ธโƒฃ JOB POSTING COVERAGE\n", " Total companies: 24,473\n", " With job posting skills: 23,528\n", " Without: 945\n", " Coverage: 96.1%\n", " โœ… Excellent (>90%)\n", "\n", "4๏ธโƒฃ EMBEDDING QUALITY\n", " Sample: 100ร—100 matrix\n", " Mean similarity: 0.2690\n", " Std: 0.1147\n", " Top 1% scores: 0.5317\n", " โœ… Good spread\n", "\n", "================================================================================\n", "๐Ÿ“Š METRICS SUMMARY\n", "================================================================================\n", "โœ… Match Score Distribution: Mean=0.573, Std=0.042\n", "โœ… Bilateral Fairness: 0.719 (ACCEPTABLE)\n", "โœ… Job Posting Coverage: 96.1%\n", "โœ… Embedding Quality: Std=0.115\n", "================================================================================\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 12: Evaluation Metrics (Precision, Bilateral Fairness, Coverage)\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "print(\"๐Ÿ“Š EVALUATION METRICS\")\n", "print(\"=\" * 80)\n", "\n", "# ============================================================================\n", "# METRIC 1: Match Score Distribution\n", "# ============================================================================\n", "print(\"\\n1๏ธโƒฃ MATCH SCORE DISTRIBUTION\")\n", "\n", "# Sample matches\n", "n_sample = min(500, len(candidates))\n", "all_scores = []\n", "\n", "for i in range(n_sample):\n", " matches = find_top_matches(i, top_k=10)\n", " scores = [score for _, score in matches]\n", " all_scores.extend(scores)\n", "\n", "print(f\" Sample size: {n_sample} candidates ร— 10 matches = {len(all_scores)} scores\")\n", "print(f\"\\n Statistics:\")\n", "print(f\" Mean: {np.mean(all_scores):.4f}\")\n", "print(f\" Median: {np.median(all_scores):.4f}\")\n", "print(f\" Std: {np.std(all_scores):.4f}\")\n", "print(f\" Min: {np.min(all_scores):.4f}\")\n", "print(f\" Max: {np.max(all_scores):.4f}\")\n", "\n", "# Histogram\n", "import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6), facecolor='#1a1a1a')\n", "ax.set_facecolor('#1a1a1a')\n", "\n", "ax.hist(all_scores, bins=50, color='#3498db', alpha=0.7, edgecolor='white')\n", "ax.set_xlabel('Match Score', color='white', fontsize=12)\n", "ax.set_ylabel('Frequency', color='white', fontsize=12)\n", "ax.set_title('Distribution of Match Scores', color='white', fontsize=14, fontweight='bold')\n", "ax.tick_params(colors='white')\n", "ax.grid(True, alpha=0.2)\n", "\n", "plt.tight_layout()\n", "plt.savefig(f'{Config.RESULTS_PATH}score_distribution.png', facecolor='#1a1a1a', dpi=150)\n", "print(f\"\\n ๐Ÿ’พ Saved: score_distribution.png\")\n", "\n", "# ============================================================================\n", "# METRIC 2: Bilateral Fairness Ratio\n", "# ============================================================================\n", "print(f\"\\n2๏ธโƒฃ BILATERAL FAIRNESS RATIO\")\n", "\n", "# Candidate โ†’ Company scores\n", "cand_to_comp_scores = []\n", "for i in range(min(200, len(candidates))):\n", " matches = find_top_matches(i, top_k=5)\n", " avg_score = np.mean([score for _, score in matches])\n", " cand_to_comp_scores.append(avg_score)\n", "\n", "# Company โ†’ Candidate scores (sample companies)\n", "comp_to_cand_scores = []\n", "for i in range(min(200, len(companies_full))):\n", " comp_vec = comp_vectors[i].reshape(1, -1)\n", " similarities = cosine_similarity(comp_vec, cand_vectors)[0]\n", " top_5_scores = np.sort(similarities)[-5:]\n", " avg_score = np.mean(top_5_scores)\n", " comp_to_cand_scores.append(avg_score)\n", "\n", "cand_avg = np.mean(cand_to_comp_scores)\n", "comp_avg = np.mean(comp_to_cand_scores)\n", "\n", "bilateral_fairness = min(cand_avg, comp_avg) / max(cand_avg, comp_avg)\n", "\n", "print(f\" Candidate โ†’ Company avg: {cand_avg:.4f}\")\n", "print(f\" Company โ†’ Candidate avg: {comp_avg:.4f}\")\n", "print(f\" Bilateral Fairness Ratio: {bilateral_fairness:.4f}\")\n", "print(f\" {'โœ… FAIR (>0.85)' if bilateral_fairness > 0.85 else '๐ŸŸก Acceptable (>0.70)' if bilateral_fairness > 0.70 else 'โŒ Imbalanced'}\")\n", "\n", "# ============================================================================\n", "# METRIC 3: Job Posting Coverage\n", "# ============================================================================\n", "print(f\"\\n3๏ธโƒฃ JOB POSTING COVERAGE\")\n", "\n", "has_real_skills = ~companies_full['required_skills'].isin(['', 'Not specified'])\n", "with_postings = has_real_skills.sum()\n", "total_companies = len(companies_full)\n", "coverage = (with_postings / total_companies) * 100\n", "\n", "print(f\" Total companies: {total_companies:,}\")\n", "print(f\" With job posting skills: {with_postings:,}\")\n", "print(f\" Without: {total_companies - with_postings:,}\")\n", "print(f\" Coverage: {coverage:.1f}%\")\n", "print(f\" {'โœ… Excellent (>90%)' if coverage > 90 else '๐ŸŸก Good (>70%)' if coverage > 70 else 'โŒ Poor'}\")\n", "\n", "# ============================================================================\n", "# METRIC 4: Embedding Quality (Cosine Similarity Stats)\n", "# ============================================================================\n", "print(f\"\\n4๏ธโƒฃ EMBEDDING QUALITY\")\n", "\n", "# Sample similarity matrix\n", "sample_size = min(100, len(cand_vectors), len(comp_vectors))\n", "sim_matrix = cosine_similarity(cand_vectors[:sample_size], comp_vectors[:sample_size])\n", "\n", "print(f\" Sample: {sample_size}ร—{sample_size} matrix\")\n", "print(f\" Mean similarity: {np.mean(sim_matrix):.4f}\")\n", "print(f\" Std: {np.std(sim_matrix):.4f}\")\n", "print(f\" Top 1% scores: {np.percentile(sim_matrix, 99):.4f}\")\n", "print(f\" {'โœ… Good spread' if np.std(sim_matrix) > 0.1 else 'โš ๏ธ Low variance'}\")\n", "\n", "# ============================================================================\n", "# SUMMARY\n", "# ============================================================================\n", "print(f\"\\n{'='*80}\")\n", "print(\"๐Ÿ“Š METRICS SUMMARY\")\n", "print(f\"{'='*80}\")\n", "print(f\"โœ… Match Score Distribution: Mean={np.mean(all_scores):.3f}, Std={np.std(all_scores):.3f}\")\n", "print(f\"โœ… Bilateral Fairness: {bilateral_fairness:.3f} {'(FAIR)' if bilateral_fairness > 0.85 else '(ACCEPTABLE)'}\")\n", "print(f\"โœ… Job Posting Coverage: {coverage:.1f}%\")\n", "print(f\"โœ… Embedding Quality: Std={np.std(sim_matrix):.3f}\")\n", "print(f\"{'='*80}\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ•ธ๏ธ CREATING INTERACTIVE NETWORK (BROWSER MODE)...\n", "================================================================================\n", "\n", "๐Ÿ“Š Network configuration:\n", " Candidates: 20\n", " Matches per candidate: 5\n", " Target: ~100 connections\n", "\n", "๐Ÿ”ต Adding candidate nodes...\n", "๐Ÿ”ด Adding company nodes & connections...\n", "\n", "โœ… Network complete!\n", " Total nodes: 68\n", " Candidates: 20\n", " Companies: 48\n", " Edges: 100\n", "\n", "๐Ÿ’พ Saved: ../results/network_interactive.html\n", " Size: 114.91 KB\n", " Full path: /home/roger/Desktop/files_to_deploy_HRHUB/hrhub_project/data/results/network_interactive.html\n", "\n", "๐ŸŒ Opening in default browser...\n", "โœ… Browser opened!\n", "\n", "================================================================================\n", "๐Ÿ’ก HOW TO USE THE INTERACTIVE GRAPH:\n", "================================================================================\n", " ๐Ÿ–ฑ๏ธ DRAG nodes to rearrange the network\n", " ๐Ÿ” SCROLL to zoom in/out\n", " ๐Ÿ‘† HOVER over nodes/edges to see detailed info\n", " ๐ŸŽฏ CLICK nodes to highlight connections\n", " โ†”๏ธ DRAG background to pan the view\n", " ๐ŸŽฎ Use NAVIGATION BUTTONS (bottom-right)\n", " โŒจ๏ธ Press 'S' to stabilize physics\n", "\n", "๐ŸŽจ VISUAL LEGEND:\n", " ๐ŸŸข Green circles = Candidates (25px)\n", " ๐Ÿ”ด Red boxes = Companies (18px)\n", " โ”โ”โ” White lines = Match connections\n", " Thicker lines = Higher match scores\n", "\n", "๐Ÿ“Š TOOLTIPS SHOW:\n", " Candidates: Category, Skills, Experience\n", " Companies: Industry, Specialties, Required Skills, Postings\n", " Edges: Match rank & score\n", "\n", "๐Ÿ’พ EXPORT:\n", " Right-click โ†’ Save image as PNG\n", " Or take screenshot for reports\n", "================================================================================\n" ] } ], "source": [ "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 11: PyVis Interactive Network - BROWSER ONLY (Full Info)\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "from pyvis.network import Network\n", "import webbrowser\n", "import os\n", "\n", "print(\"๐Ÿ•ธ๏ธ CREATING INTERACTIVE NETWORK (BROWSER MODE)...\")\n", "print(\"=\" * 80)\n", "\n", "# ============================================================================\n", "# Configuration\n", "# ============================================================================\n", "n_cand_sample = 20 # 20 candidates\n", "top_k_per_cand = 5 # Top 5 matches each\n", "\n", "print(f\"\\n๐Ÿ“Š Network configuration:\")\n", "print(f\" Candidates: {n_cand_sample}\")\n", "print(f\" Matches per candidate: {top_k_per_cand}\")\n", "print(f\" Target: ~{n_cand_sample * top_k_per_cand} connections\")\n", "\n", "# ============================================================================\n", "# Initialize PyVis Network\n", "# ============================================================================\n", "net = Network(\n", " height='900px',\n", " width='100%',\n", " bgcolor='#1a1a1a',\n", " font_color='white',\n", " notebook=False, # Browser mode\n", " cdn_resources='remote'\n", ")\n", "\n", "# Physics for nice layout\n", "net.set_options(\"\"\"\n", "var options = {\n", " \"physics\": {\n", " \"forceAtlas2Based\": {\n", " \"gravitationalConstant\": -50,\n", " \"centralGravity\": 0.01,\n", " \"springLength\": 200,\n", " \"springConstant\": 0.08,\n", " \"avoidOverlap\": 1\n", " },\n", " \"maxVelocity\": 30,\n", " \"solver\": \"forceAtlas2Based\",\n", " \"timestep\": 0.35,\n", " \"stabilization\": {\n", " \"enabled\": true,\n", " \"iterations\": 150\n", " }\n", " },\n", " \"nodes\": {\n", " \"font\": {\n", " \"size\": 16,\n", " \"color\": \"white\",\n", " \"face\": \"arial\"\n", " },\n", " \"borderWidth\": 2\n", " },\n", " \"edges\": {\n", " \"smooth\": {\n", " \"enabled\": true,\n", " \"type\": \"continuous\"\n", " },\n", " \"width\": 2\n", " },\n", " \"interaction\": {\n", " \"hover\": true,\n", " \"tooltipDelay\": 50,\n", " \"navigationButtons\": true,\n", " \"keyboard\": {\n", " \"enabled\": true\n", " },\n", " \"zoomView\": true,\n", " \"dragView\": true\n", " }\n", "}\n", "\"\"\")\n", "\n", "print(f\"\\n๐Ÿ”ต Adding candidate nodes...\")\n", "\n", "# ============================================================================\n", "# Add Candidate Nodes (GREEN CIRCLES)\n", "# ============================================================================\n", "companies_added = set()\n", "\n", "for i in range(min(n_cand_sample, len(candidates))):\n", " cand = candidates.iloc[i]\n", " \n", " # Build rich tooltip\n", " category = cand.get('Category', 'Unknown')\n", " skills = str(cand.get('skills', 'N/A'))\n", " if isinstance(skills, list):\n", " skills = ', '.join(skills[:5]) # First 5 skills\n", " else:\n", " skills = skills[:150]\n", " \n", " experience = str(cand.get('positions', 'N/A'))[:100]\n", " \n", " tooltip = f\"\"\"\n", "
\n", "

๐Ÿ‘ค Candidate {i}

\n", "
\n", "

Category: {category}

\n", "

Top Skills:
{skills}...

\n", "

Experience:
{experience}...

\n", "
\n", " \"\"\"\n", " \n", " net.add_node(\n", " f\"C{i}\",\n", " label=f\"Candidate {i}\",\n", " title=tooltip,\n", " color='#2ecc71',\n", " size=25,\n", " shape='dot',\n", " borderWidth=2,\n", " borderWidthSelected=4\n", " )\n", "\n", "print(f\"๐Ÿ”ด Adding company nodes & connections...\")\n", "\n", "# ============================================================================\n", "# Add Company Nodes (RED SQUARES) & Edges\n", "# ============================================================================\n", "edge_count = 0\n", "\n", "for cand_idx in range(min(n_cand_sample, len(candidates))):\n", " matches = find_top_matches(cand_idx, top_k=top_k_per_cand)\n", " \n", " for rank, (comp_idx, score) in enumerate(matches, 1):\n", " comp_id = f\"CO{comp_idx}\"\n", " \n", " # Add company node if not added yet\n", " if comp_id not in companies_added:\n", " comp = companies_full.iloc[comp_idx]\n", " \n", " name = comp.get('name', 'Unknown Company')\n", " industry = str(comp.get('industries_list', 'N/A'))[:80]\n", " specialties = str(comp.get('specialties_list', 'N/A'))[:80]\n", " required_skills = str(comp.get('required_skills', 'N/A'))[:150]\n", " total_postings = comp.get('total_postings', 0)\n", " \n", " # Rich company tooltip\n", " tooltip = f\"\"\"\n", "
\n", "

๐Ÿข {name}

\n", "
\n", "

Industry: {industry}

\n", "

Specialties: {specialties}

\n", "

Required Skills:
{required_skills}...

\n", "

Total Job Postings: {total_postings}

\n", "
\n", " \"\"\"\n", " \n", " net.add_node(\n", " comp_id,\n", " label=name[:20] + ('...' if len(name) > 20 else ''),\n", " title=tooltip,\n", " color='#e74c3c',\n", " size=18,\n", " shape='box',\n", " borderWidth=2\n", " )\n", " companies_added.add(comp_id)\n", " \n", " # Add edge with rich info\n", " edge_tooltip = f\"\"\"\n", "
\n", " Match Quality
\n", " Rank: #{rank}
\n", " Score: {score:.3f}
\n", " {'๐Ÿ”ฅ Excellent' if score > 0.7 else 'โœ… Good' if score > 0.5 else '๐ŸŸก Moderate'}\n", "
\n", " \"\"\"\n", " \n", " net.add_edge(\n", " f\"C{cand_idx}\",\n", " comp_id,\n", " value=float(score * 10),\n", " title=edge_tooltip,\n", " color={'color': '#95a5a6', 'opacity': 0.6}\n", " )\n", " edge_count += 1\n", "\n", "print(f\"\\nโœ… Network complete!\")\n", "print(f\" Total nodes: {len(net.nodes)}\")\n", "print(f\" Candidates: {n_cand_sample}\")\n", "print(f\" Companies: {len(companies_added)}\")\n", "print(f\" Edges: {edge_count}\")\n", "\n", "# ============================================================================\n", "# Save HTML\n", "# ============================================================================\n", "html_file = f'{Config.RESULTS_PATH}network_interactive.html'\n", "net.save_graph(html_file)\n", "\n", "abs_path = os.path.abspath(html_file)\n", "file_size = os.path.getsize(html_file) / 1024\n", "\n", "print(f\"\\n๐Ÿ’พ Saved: {html_file}\")\n", "print(f\" Size: {file_size:.2f} KB\")\n", "print(f\" Full path: {abs_path}\")\n", "\n", "# ============================================================================\n", "# Open in browser\n", "# ============================================================================\n", "print(f\"\\n๐ŸŒ Opening in default browser...\")\n", "\n", "try:\n", " webbrowser.open(f'file://{abs_path}')\n", " print(f\"โœ… Browser opened!\")\n", "except Exception as e:\n", " print(f\"โš ๏ธ Auto-open failed: {e}\")\n", " print(f\"\\n๐Ÿ“‹ Manual open:\")\n", " print(f\" Firefox/Chrome โ†’ Open File โ†’ {abs_path}\")\n", "\n", "# ============================================================================\n", "# Usage guide\n", "# ============================================================================\n", "print(f\"\\n{'='*80}\")\n", "print(\"๐Ÿ’ก HOW TO USE THE INTERACTIVE GRAPH:\")\n", "print(f\"{'='*80}\")\n", "print(\" ๐Ÿ–ฑ๏ธ DRAG nodes to rearrange the network\")\n", "print(\" ๐Ÿ” SCROLL to zoom in/out\")\n", "print(\" ๐Ÿ‘† HOVER over nodes/edges to see detailed info\")\n", "print(\" ๐ŸŽฏ CLICK nodes to highlight connections\")\n", "print(\" โ†”๏ธ DRAG background to pan the view\")\n", "print(\" ๐ŸŽฎ Use NAVIGATION BUTTONS (bottom-right)\")\n", "print(\" โŒจ๏ธ Press 'S' to stabilize physics\")\n", "print(f\"\\n๐ŸŽจ VISUAL LEGEND:\")\n", "print(\" ๐ŸŸข Green circles = Candidates (25px)\")\n", "print(\" ๐Ÿ”ด Red boxes = Companies (18px)\")\n", "print(\" โ”โ”โ” White lines = Match connections\")\n", "print(\" Thicker lines = Higher match scores\")\n", "print(f\"\\n๐Ÿ“Š TOOLTIPS SHOW:\")\n", "print(\" Candidates: Category, Skills, Experience\")\n", "print(\" Companies: Industry, Specialties, Required Skills, Postings\")\n", "print(\" Edges: Match rank & score\")\n", "print(f\"\\n๐Ÿ’พ EXPORT:\")\n", "print(\" Right-click โ†’ Save image as PNG\")\n", "print(\" Or take screenshot for reports\")\n", "print(\"=\" * 80)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "๐Ÿ’พ SAVING FINAL MODELS FOR PRODUCTION...\n", "================================================================================\n", "\n", "1๏ธโƒฃ EMBEDDINGS\n", " โœ… candidate_embeddings.npy (exists)\n", " โœ… company_embeddings.npy (exists)\n", " โœ… candidates_metadata.pkl (exists)\n", " โœ… companies_metadata.pkl (exists)\n", "\n", "2๏ธโƒฃ MODEL INFORMATION\n", " ๐Ÿ’พ model_info.json\n", "\n", "3๏ธโƒฃ DEPLOYMENT PACKAGE\n", " โœ… candidate_embeddings.npy: 13.98 MB\n", " โœ… company_embeddings.npy: 35.85 MB\n", " โœ… candidates_metadata.pkl: 2.33 MB\n", " โœ… companies_metadata.pkl: 29.10 MB\n", " โœ… model_info.json: 0.00 MB\n", "\n", " ๐Ÿ“ฆ Total: 81.26 MB\n", "\n", "4๏ธโƒฃ VISUALIZATION FILES\n", " โœ… network_interactive.html: 114.91 KB\n", " โœ… score_distribution.png: 37.94 KB\n", "\n", "================================================================================\n", "๐ŸŽฏ DEPLOYMENT READY!\n", "================================================================================\n", "\n", "๐Ÿ“‚ Location: ../processed/\n", "๐Ÿ“ฆ Total size: 81.26 MB\n", "\n", "โœ… Ready for:\n", " - Streamlit GUI\n", " - FastAPI deployment\n", " - Production inference\n", "\n", "๐Ÿš€ Next step: Build Streamlit app!\n", "================================================================================\n" ] } ], "source": [ "# %%\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "# CELL 13: Save Final Models for Production\n", "# โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•\n", "\n", "print(\"๐Ÿ’พ SAVING FINAL MODELS FOR PRODUCTION...\")\n", "print(\"=\" * 80)\n", "\n", "# ============================================================================\n", "# Verify/Save embeddings\n", "# ============================================================================\n", "print(\"\\n1๏ธโƒฃ EMBEDDINGS\")\n", "\n", "files_to_save = {\n", " 'candidate_embeddings.npy': cand_vectors,\n", " 'company_embeddings.npy': comp_vectors,\n", " 'candidates_metadata.pkl': candidates,\n", " 'companies_metadata.pkl': companies_full\n", "}\n", "\n", "for filename, data in files_to_save.items():\n", " filepath = f'{Config.PROCESSED_PATH}{filename}'\n", " \n", " if os.path.exists(filepath):\n", " print(f\" โœ… {filename} (exists)\")\n", " else:\n", " if filename.endswith('.npy'):\n", " np.save(filepath, data)\n", " else:\n", " data.to_pickle(filepath)\n", " print(f\" ๐Ÿ’พ {filename} (saved)\")\n", "\n", "# ============================================================================\n", "# Save model info\n", "# ============================================================================\n", "print(\"\\n2๏ธโƒฃ MODEL INFORMATION\")\n", "\n", "model_info = {\n", " 'model_name': Config.EMBEDDING_MODEL,\n", " 'embedding_dim': 384,\n", " 'n_candidates': len(candidates),\n", " 'n_companies': len(companies_full),\n", " 'bilateral_fairness': float(bilateral_fairness),\n", " 'coverage_pct': float(coverage),\n", " 'mean_match_score': float(np.mean(all_scores))\n", "}\n", "\n", "model_path = f'{Config.PROCESSED_PATH}model_info.json'\n", "with open(model_path, 'w') as f:\n", " json.dump(model_info, f, indent=2)\n", "\n", "print(f\" ๐Ÿ’พ model_info.json\")\n", "\n", "# ============================================================================\n", "# Package summary\n", "# ============================================================================\n", "print(\"\\n3๏ธโƒฃ DEPLOYMENT PACKAGE\")\n", "\n", "deployment_files = [\n", " 'candidate_embeddings.npy',\n", " 'company_embeddings.npy',\n", " 'candidates_metadata.pkl',\n", " 'companies_metadata.pkl',\n", " 'model_info.json'\n", "]\n", "\n", "total_size = 0\n", "for f in deployment_files:\n", " path = f'{Config.PROCESSED_PATH}{f}'\n", " if os.path.exists(path):\n", " size_mb = os.path.getsize(path) / (1024 * 1024)\n", " total_size += size_mb\n", " print(f\" โœ… {f}: {size_mb:.2f} MB\")\n", "\n", "print(f\"\\n ๐Ÿ“ฆ Total: {total_size:.2f} MB\")\n", "\n", "# ============================================================================\n", "# Visualization files\n", "# ============================================================================\n", "print(\"\\n4๏ธโƒฃ VISUALIZATION FILES\")\n", "\n", "viz_files = [\n", " 'network_interactive.html',\n", " 'score_distribution.png'\n", "]\n", "\n", "for f in viz_files:\n", " path = f'{Config.RESULTS_PATH}{f}'\n", " if os.path.exists(path):\n", " size_kb = os.path.getsize(path) / 1024\n", " print(f\" โœ… {f}: {size_kb:.2f} KB\")\n", "\n", "# ============================================================================\n", "# Final summary\n", "# ============================================================================\n", "print(f\"\\n{'='*80}\")\n", "print(\"๐ŸŽฏ DEPLOYMENT READY!\")\n", "print(f\"{'='*80}\")\n", "print(f\"\\n๐Ÿ“‚ Location: {Config.PROCESSED_PATH}\")\n", "print(f\"๐Ÿ“ฆ Total size: {total_size:.2f} MB\")\n", "print(f\"\\nโœ… Ready for:\")\n", "print(f\" - Streamlit GUI\")\n", "print(f\" - FastAPI deployment\")\n", "print(f\" - Production inference\")\n", "print(f\"\\n๐Ÿš€ Next step: Build Streamlit app!\")\n", "print(\"=\" * 80)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }