End-to-End_AI_Career_Coach

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End-to-End_AI_Career_Coach

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This is the official repository for Group "Underfitted 4 (U4)" for the Machine Learning Capstone Project. This is an End-to-end AI Career Coach: MLP surrogates + PPO reinforcement learning + LLM report generation for personalized, cost-aware campus placement advising.

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# U4 AI Career Coach

**End-to-End AI Career Coach: MLP Surrogates + PPO Reinforcement Learning + LLM Report Generation**

> An AI-powered career advising system that goes beyond prediction — it tells students *what to do, in what order, and why*, with realistic effort constraints and empathetic natural-language coaching reports.

[![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/)
[![Streamlit](https://img.shields.io/badge/Streamlit-app-FF4B4B.svg)](https://streamlit.io/)

---

## Overview

Traditional career prediction models tell a student *"you have a 35% chance of getting placed"* — and stop there. Our system answers the follow-up question: **"What precise steps should I take to change this outcome?"**

We formulate career development as a **Markov Decision Process (MDP)** and solve it with **Proximal Policy Optimization (PPO)**. The system:

1. **Diagnoses** — Dual MLPs predict placement probability and expected salary
2. **Plans** — PPO discovers the optimal multi-step improvement path under realistic effort constraints
3. **Communicates** — An LLM (Gemini Pro) translates the mathematical trajectory into an empathetic, professional coaching report

## Key Features

- **Cost-Aware Planning**: Exponential effort decay (λ=0.8) prevents unrealistic recommendations like "do 10 internships"
- **Dual-Policy Architecture**: Separate PPO policies for unemployed (threshold crossing) and employed (salary maximisation) students
- **Privacy-Preserving Reports**: A desensitisation layer conceals all model internals (thresholds, logits, raw scores) from the LLM and end user
- **Interactive Web App**: Streamlit-based UI — enter your profile, get a personalised coaching report in ~30 seconds

## Results at a Glance

| Metric | Value |
|--------|-------|
| Unemployed → Employed conversion rate | **53.9%** (avg 6.1 steps) |
| Employed salary improvement (median) | **+16.4%** |
| MLP placement AUC | **0.833** |
| MLP salary R² | **0.794** |
| End-to-end latency | **15–30 seconds** |

## System Architecture

```
Student Profile ──→ Dual-MLP Surrogate ──→ PPO RL Agent ──→ Desensitisation ──→ Gemini Pro LLM
                    (Placement + Salary)    (13 actions,      (threshold →%,       (3-chapter
                                             15-step horizon,  LPA → GBP)           coaching report)
                                             24 rollouts)
```

## Repository Structure

```
MachineLearning_2526_U4/
├── src/
│   ├── data.py                    # Data loading, preprocessing, DataLoader construction
│   ├── MLP.py                     # PlacementMLP + SalaryMLP training
│   ├── RL.py                      # MDP environment, PPO training, multi-rollout inference
│   ├── LLM.py                     # Data desensitisation + Gemini Pro report generation
│   ├── app.py                     # Streamlit web application
│   ├── compare_models.py          # MLP vs Random Forest benchmarking
│   ├── generate_rl_figures.py     # RL evaluation figure generation
│   ├── models/                    # Trained model weights (.pth, .zip)
│   ├── figures/                   # Generated figures for the paper
│   └── ppo_tensorboard/           # PPO training logs
├── data/                          # Dataset (not tracked by git)
├── U4_Career_Coach_Report_Employed.pdf      # Example report (employed student)
├── U4_Career_Coach_Report_Unemployed.pdf    # Example report (unemployed student)
├── requirements.txt
└── README.md
```

## Quick Start

### 1. Clone & Install

```bash
git clone https://github.com/pangxuan945/MachineLearning_2526_U4.git
cd MachineLearning_2526_U4
pip install -r requirements.txt
```

### 2. Set Up API Key

Create a `.env` file in the `src/` directory:

```bash
echo 'GEMINI_API_KEY=your_gemini_api_key_here' > src/.env
```

### 3. Train Models (or use pre-trained weights)

Pre-trained weights are included in `src/models/`. To retrain from scratch:

```bash
cd src

# Step 1: Train dual MLPs
python MLP.py

# Step 2: Train PPO policies
python RL.py
```

### 4. Launch the Web App

```bash
cd src
streamlit run app.py
```

Open `http://localhost:8502` in your browser, fill in your information, and receive your coaching report.

## Example Reports

Two complete AI-generated coaching reports are available in the repository:

- 📋 [**Employed Student Report**](U4_Career_Coach_Report_Employed.pdf) — "Workplace High-Potential" with 215.6% competitiveness, 15-step salary optimisation reaching £47,739 (+12.5%)
- 📋 [**Unemployed Student Report**](U4_Career_Coach_Report_Unemployed.pdf) — "Job-seeking Breakthrough Candidate" at 53.5%, 13-step path crossing the employment threshold

## Technical Details

### Surrogate Environment
- **PlacementMLP**: 40→128→64→1, BatchNorm, Dropout(0.3/0.2), class-balanced BCE
- **SalaryMLP**: 40→128→64→1, BatchNorm, Dropout(0.1/0), Smooth L1 loss, trained on placed students only
- Threshold optimised via macro-F1 sweep on validation set

### RL Agent
- **Algorithm**: PPO (Stable-Baselines3)
- **Action space**: 13 discrete actions (12 increase + 1 decrease_backlogs)
- **Effort cost**: Percentile-based steps with geometric decay λ=0.8 per repeated use
- **Integer features**: Bernoulli jump mechanism preserving expected effort parity
- **Inference**: 24 candidate rollouts → task-specific best-path selection

### LLM Integration
- **Model**: Gemini Pro via Google GenAI API
- **Desensitisation**: Threshold normalised to 100% baseline; salaries converted LPA→GBP (×3000)
- **Guardrails**: Explicit prohibition of technical terms (MLP, RL, threshold, logit)

## Team — Underfitted 4 (U4)

| Name |
|------|
| Yihang Liu |
| Leonard Huang (Zhenxuan Huang)|
| Zifan Song |
| Shen Wu |

UCL Machine Learning Capstone Project, 2025–2026

## Citation

```bibtex
@article{liu2026career,
  title={End-to-End AI Career Coach: Integrating MLP, Reinforcement Learning, and LLM-based Explanation},
  author={Liu, Yihang and Huang, Leonard and Song, Zifan and Wu, Shen},
  year={2026},
  url={https://github.com/pangxuan945/MachineLearning_2526_U4}
}
```

## 📝 License

This project is for academic purposes as part of the UCL ELEC0149 Introduction to Machine Learning course (2025–2026).

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