UC00208 Resume-to-Job Matching Assistant (Basil Behanan)

[Basilbe]

No description provided.

[Copilot]

Pull request overview

Adds a notebook-based “Resume-to-Job Matching Assistant” prototype under Playground/Basil that compares a resume to job postings using TF‑IDF cosine similarity plus simple skill-overlap scoring, and exports ranked results.

Changes:

• Added a Jupyter notebook implementing preprocessing, similarity scoring, skill extraction, ranking, and export.
• Added sample input data (resume.txt, jobs.csv) and generated output artifacts (CSVs + chart).
• Added project README and a minimal requirements.txt for running the notebook.

Reviewed changes

Copilot reviewed 8 out of 9 changed files in this pull request and generated 9 comments.

Show a summary per file

File	Description
Playground/Basil/requirements.txt	Adds Python dependencies needed to run the notebook.
Playground/Basil/Readme.md	Documents the prototype, workflow, and expected inputs/outputs.
Playground/Basil/notebooks/resume_job_matching.ipynb	Core implementation of preprocessing, matching/scoring, visualization, and exports.
Playground/Basil/data/resume.txt	Provides a resume text input for matching (currently contains PII).
Playground/Basil/data/jobs.csv	Provides sample job postings input data.
Playground/Basil/data/temp	Adds an empty placeholder file.
Playground/Basil/outputs/ranked_jobs.csv	Generated ranked job output (includes list-like fields).
Playground/Basil/outputs/final_prototype_results.csv	Generated “final results” output (more presentation-friendly).
Playground/Basil/outputs/prototype_results_chart.png	Generated visualization artifact.

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[Copilot]

The README project structure block appears to describe a different top-level folder ("resume_job_matcher/") and includes a "src/" directory that doesn't exist under Playground/Basil. Please update the structure example to match the actual committed layout (or add the missing folder) to avoid misleading run instructions.

[Copilot]

requirements.txt lists unpinned dependencies. For reproducible notebooks (especially scikit-learn/nltk), please pin versions (or at least minimum versions) so results are stable across environments and future installs.

Suggested change

	pandas
	numpy
	matplotlib
	scikit-learn
	nltk
	jupyter
	pandas==2.2.2
	numpy==1.26.4
	matplotlib==3.8.4
	scikit-learn==1.4.2
	nltk==3.8.1
	jupyter==1.0.0

[Copilot]

This file includes personal identifiable information (phone number, email). Please remove/redact PII before committing (e.g., replace with a synthetic/sample resume) to avoid leaking sensitive data in the repository history.

Suggested change

	Tel: +61 468410310
	Email: Basilbehanan5@gmail.com
	LinkedIn: LinkedIn
	Tel: +61 400 000 000
	Email: sample.resume@example.com
	LinkedIn: linkedin.com/in/sample-profile

[Copilot]

Notebook output logs print the resume preview including PII (name/phone/email). Please clear cell outputs before committing and avoid printing raw resume content (or use a redacted/synthetic sample) to prevent PII from being stored in git history.

[Copilot]

The notebook currently contains environment-specific NLTK download output (including a local Windows user path). Consider clearing outputs before committing and making the downloads quieter/conditional (or documenting an offline setup) to keep the notebook reproducible and reduce noisy diffs.

Suggested change

	"outputs": [
	{
	"name": "stderr",
	"output_type": "stream",
	"text": [
	"[nltk_data] Downloading package punkt to\n",
	"[nltk_data] C:\\Users\\bazil\\AppData\\Roaming\\nltk_data...\n",
	"[nltk_data] Package punkt is already up-to-date!\n",
	"[nltk_data] Downloading package stopwords to\n",
	"[nltk_data] C:\\Users\\bazil\\AppData\\Roaming\\nltk_data...\n",
	"[nltk_data] Package stopwords is already up-to-date!\n"
	]
	}
	],
	"source": [
	"nltk.download(\"punkt\")\n",
	"nltk.download(\"stopwords\")\n",
	"\n",
	"outputs": [],
	"source": [
	"def ensure_nltk_resource(resource_path, download_name):\n",
	" try:\n",
	" nltk.data.find(resource_path)\n",
	" except LookupError:\n",
	" nltk.download(download_name, quiet=True)\n",
	"\n",
	"ensure_nltk_resource(\"tokenizers/punkt\", \"punkt\")\n",
	"ensure_nltk_resource(\"corpora/stopwords\", \"stopwords\")\n",
	"\n",

[Copilot]

The file data/temp is empty and looks like a placeholder/accidental artifact. Please remove it, or if it's meant to keep the directory in git, rename to a conventional placeholder like .gitkeep with an explanatory comment in the README.

Suggested change

	This placeholder file exists only to keep the `data` directory in version control.
	If repository-wide file operations are allowed, prefer renaming this file to `.gitkeep`
	and documenting that convention in the README.

[Copilot]

Skill extraction may miss skills containing punctuation because preprocessing strips punctuation (e.g., "scikit-learn" becomes "scikitlearn"), but skill_list still contains the hyphenated form. Normalize skill_list entries with the same preprocessing (or use token/regex-based matching) so skills like scikit-learn are detected reliably.

[Copilot]

The notebook includes a large embedded base64 image output and other executed cell outputs, which makes diffs noisy and increases repo size. Consider clearing outputs before committing (e.g., via nbstripout/jupyter "Clear All Outputs") and relying on the saved CSV/chart artifacts (or regenerate them during execution).

[Copilot]

ranked_jobs.csv is exported directly from jobs_df while it still contains Python list-valued columns (e.g., job_skills/matched_skills/missing_skills). In the CSV these become stringified lists (e.g., "['python', ...']"), which is harder to consume consistently. Consider normalizing these columns to a consistent serialized format (comma-separated strings or JSON) before exporting.

Suggested change

	"ranked_jobs.to_csv(\"../outputs/ranked_jobs.csv\", index=False)\n",
	"final_results.to_csv(\"../outputs/final_prototype_results.csv\", index=False)\n",
	"import json\n",
	"\n",
	"def _serialize_list_columns_for_csv(df):\n",
	" export_df = df.copy()\n",
	" for column in export_df.columns:\n",
	" if export_df[column].apply(lambda value: isinstance(value, list)).any():\n",
	" export_df[column] = export_df[column].apply(\n",
	" lambda value: json.dumps(value) if isinstance(value, list) else value\n",
	" )\n",
	" return export_df\n",
	"\n",
	"ranked_jobs_export = _serialize_list_columns_for_csv(ranked_jobs)\n",
	"final_results_export = _serialize_list_columns_for_csv(final_results)\n",
	"\n",
	"ranked_jobs_export.to_csv(\"../outputs/ranked_jobs.csv\", index=False)\n",
	"final_results_export.to_csv(\"../outputs/final_prototype_results.csv\", index=False)\n",