🧭 Tech Stack Recommender — Intelligent Career Role Recommendation Engine

A Python-based recommendation engine that analyzes a user's technical skills and recommends the most suitable software engineering roles using a transparent skill-matching algorithm. Designed as a lightweight, explainable career recommendation system.

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📖 Table of Contents

• Overview
• Problem Statement
• Architecture
• Project Structure
• Installation
• Usage
• Example Output
• Recommendation Methodology
• CLI Arguments
• Testing
• Tech Stack
• Future Improvements
• License

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📌 Overview

Choosing the right software engineering career path can be difficult when multiple roles share overlapping skills.

This project simulates an intelligent career recommendation engine. Instead of relying on hard-coded career suggestions, it compares a user's skills against a dataset of job roles, calculates a percentage match for each role, ranks the results, and explains why each recommendation was made.

The application supports interactive mode, command-line arguments, and a demonstration mode for quickly showcasing the recommendation engine.

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🎯 Problem Statement

Design a lightweight recommendation engine that:

• Accepts a user's technical skills
• Compares them with predefined job-role requirements
• Calculates a matching score
• Returns the most suitable career paths ranked by relevance

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🏗 Architecture

User Skills
      │
      ▼
Input Validation
      │
      ▼
Skill Normalization
      │
      ▼
Load Job Dataset
      │
      ▼
Skill Matching Engine
      │
      ▼
Percentage Match Calculation
      │
      ▼
Ranking (Top-K)
      │
      ▼
Formatted CLI Output

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🗂 Project Structure

tech-stack-recommender/
├── data/
│   └── raw_skills.csv
├── src/
│   └── recommender.py
├── tests/
│   └── test_recommender.py
├── main.py
├── README.md
├── requirements.txt
├── LICENSE
└── .gitignore

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💻 Installation

git clone https://github.com/<your-username>/tech-stack-recommender.git
cd tech-stack-recommender

python -m venv venv

# Windows
venv\Scripts\activate

# Linux / macOS
source venv/bin/activate

pip install -r requirements.txt

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🚀 Usage

Interactive mode

python main.py

CLI mode

python main.py --skills "Python,Docker,AWS" --top 3

Demo mode

python main.py --demo

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📊 Example Output

Input Skills:
Python, Cloud Computing, Automation

1. DevOps Engineer
Match: 87%

Required Skills:
Python
Cloud Computing
CI/CD
Automation

Description:
Builds and maintains deployment pipelines.

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🧠 Recommendation Methodology

Each job role is represented as a collection of required skills.

The recommendation score is computed as:

Match % =
(Matching Skills ÷ Required Skills) × 100

The engine:

1. Loads the dataset.
2. Normalizes user input.
3. Compares every role.
4. Calculates percentage overlap.
5. Sorts results.
6. Returns the Top-K recommendations.

This transparent approach makes every recommendation explainable rather than a black-box prediction.

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⚙ CLI Arguments

Argument	Description
--skills	Comma-separated list of skills
--top	Number of recommendations
--demo	Run predefined demonstrations

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🧪 Testing

Run unit tests:

python -m unittest discover tests

Tests validate:

• Recommendation scoring
• Ranking logic
• Input handling
• Result formatting

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🛠 Tech Stack

Category	Technology
Language	Python 3.8+
Interface	Command Line
Dataset	CSV
Testing	unittest
Dependencies	Python Standard Library

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🔮 Future Improvements

• Semantic skill matching using Sentence Transformers
• Resume parsing
• Skill-gap analysis
• Learning roadmap generation
• FastAPI backend
• Streamlit web interface
• LLM-powered career recommendations
• Recommendation explanations using AI

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.