Complete End-to-End Neural Network Project: UK Student Attainment Forecasting
Design and implement a comprehensive Python project that trains a neural network to forecast a UK student's next-year Attainment 8 score, UCAS tariff points, or GCSE 9-1 grades from their prior attainment history and contextual factors.
1. Data Input & Management
Primary Data Sources

CSV Input: Accept files with columns:

student_id (anonymised PMR or hashed ULN for GDPR compliance)
academic_year (e.g., 2019, 2020, 2021)
subject (Mathematics, English Literature, Biology, etc.)
grade (9-1 scale for GCSE or A-level letter/number)
points (GCSE 9=9 points, A-level A*=56 points)


Contextual Features from National Pupil Database:

fsm_eligible (Free School Meals status)
eal_status (English as Additional Language)
idaci_decile (Income Deprivation Affecting Children Index)
region (North East, London, etc.)
gender (Male/Female/Other)
ethnicity (White British, Black African, etc.)
sen_status (Special Educational Needs)



Manual Grade Entry System

Web Interface: Create a Flask/Streamlit application for manual data entry
Form Features:

Student selection dropdown with autocomplete
Subject and grade entry with real-time validation
Bulk import from clipboard (tab-separated values)
Grade conversion helper (A*-G to 9-1 scale)


Data Validation:

Check grade ranges (1-9 for GCSE, A*-U for A-levels)
Validate academic year sequences
Flag anomalous grade patterns


Export Functionality: Convert manual entries to CSV format compatible with main pipeline
Audit Trail: Log all entries with timestamps and user identification

2. Data Pre-processing Pipeline
Temporal Sequence Construction

Build rolling 3-5 year progression sequences:

KS2 → KS3 → KS4 → predicted KS5
Handle variable-length sequences with appropriate padding


Account for students entering/leaving the system mid-progression

Grade Harmonisation & Conversion

Legacy Grade Mapping: Convert A*-G grades to 9-1 scale:

A* → 8.5, A → 7, B → 6, C → 5, D → 4, E → 3, F → 2, G → 1


Missing Data Handling:

Subject-specific grade imputation using cohort medians
Temporal interpolation for missing academic years
Flag imputed values for model awareness



Feature Engineering

Numerical Features: Z-score normalisation (mean=0, std=1)
Categorical Encoding: One-hot encoding for subjects and regions
Derived Features:

Grade trajectory (improving/declining patterns)
Subject diversity index
Achievement consistency score


Interaction Terms: FSM × EAL, IDACI × Region combinations

Data Splitting Strategy

Student-level splitting to prevent temporal leakage:

Train: 70% of students
Validation: 15% of students
Test: 15% of students


Stratified sampling by demographic groups to ensure representation

3. Neural Network Architecture
Model Design

Hybrid LSTM-Transformer Architecture:

Embedding layer for subjects (learnable 64-dim vectors)
LSTM layer for temporal sequence modeling (hidden_size: 128-512)
Multi-head attention mechanism for subject relationships
Demographic feature integration via concatenation


Input Processing:

Variable-length sequence masking for padding tokens
Positional encodings for academic year information
Separate pathways for academic and contextual features



Output Configurations

Option A - Regression: Single scalar output for Attainment 8 score (0-90 range)
Option B - Classification: Multi-task heads for each subject's grade probabilities (9 classes: 1-9)
Option C - Hybrid: Combined regression and classification with shared representations

Regularisation Techniques

Dropout layers (0.1-0.5 rates)
Layer normalisation after each major component
Weight decay regularisation
Gradient clipping (max_norm=1.0)

4. Training & Hyperparameter Optimisation
Loss Functions

Regression: Weighted Mean Squared Error (higher weights for recent grades)
Classification: Focal Loss to handle grade distribution imbalance
Multi-task: Weighted combination with learnable task weights

Optimisation Strategy

Optimiser: AdamW with cosine annealing scheduler
Learning Rate: Initial range 1e-5 to 1e-2
Weight Decay: 1e-4 for regularisation
Batch Size: 32-128 (memory permitting)

Bayesian Hyperparameter Search (Optuna)

Search Space:

LSTM hidden dimensions: [64, 128, 256, 512]
Attention heads: [4, 8, 16]
Dropout rates: [0.1, 0.2, 0.3, 0.5]
Learning rates: [1e-5, 1e-4, 1e-3, 1e-2]
Batch sizes: [32, 64, 128]


Objective: Minimise validation RMSE (regression) or maximise balanced accuracy (classification)
Early Stopping: Patience=10 epochs on validation metric

Model Checkpointing

Save best model as best_model.pt
Export hyperparameters to config.yaml
Store training history and metrics

5. Comprehensive Evaluation & Fairness Analysis
Performance Metrics

Regression:

Root Mean Square Error (RMSE)
Mean Absolute Error (MAE)
Mean Absolute Percentage Error (MAPE)
R-squared coefficient


Classification:

Balanced accuracy (macro-average)
F1-score (macro and micro)
ROC-AUC per class
Confusion matrices



Fairness & Bias Assessment

Disaggregated Analysis by protected characteristics:

FSM eligible vs non-eligible
EAL vs native English speakers
Gender groups
Ethnic categories
SEN vs non-SEN students


Geographic Analysis: Performance across regions and IDACI deciles
Intersectionality: Combined protected characteristic analysis

Statistical Validation

Significance Testing: Bootstrap confidence intervals (95%) for all metrics
Calibration Analysis: Reliability diagrams for probability outputs
Residual Analysis: Q-Q plots and residual vs fitted plots
Feature Importance: SHAP values for model interpretability

Visualisation Requirements

Performance dashboards with interactive plots
Residual analysis by demographic groups
Grade distribution comparisons (predicted vs actual)
Temporal performance trends

6. Production Deployment & Monitoring
Model Serving

API Development: FastAPI endpoint with automatic documentation
Input Validation: Pydantic models for request/response schemas
Scalability: Docker containerisation with horizontal scaling
Response Format: JSON with predictions, confidence intervals, and explanations

Monitoring & Maintenance

Model Drift Detection: Statistical tests on input distributions
Performance Tracking: Real-time accuracy monitoring
Data Quality Checks: Automated anomaly detection pipeline
Retraining Triggers: Performance degradation alerts

Compliance & Governance

GDPR Compliance:

Data anonymisation verification
Right to erasure implementation
Audit trail maintenance


DfE Data Standards: Adherence to school data handling guidelines
Explainability: Individual prediction explanations for stakeholders

7. Advanced Features & Extensions
Uncertainty Quantification

Monte Carlo Dropout: Generate prediction intervals
Ensemble Methods: Combine multiple model architectures
Bayesian Neural Networks: Uncertainty in model parameters

Transfer Learning & Adaptation

Pre-training: Historical cohort data for foundation knowledge
Fine-tuning: Adaptation to specific schools or regions
Few-shot Learning: Predictions for students with limited history

Real-time Integration

Streaming Updates: Incremental learning as new grades arrive
Active Learning: Identify students needing intervention
Federated Learning: Multi-school collaboration while preserving privacy

Deliverables

Complete Python Package with modular architecture
Web Application for manual data entry and predictions
Trained Models with comprehensive evaluation reports
Documentation including API reference and user guides
Deployment Scripts for production environment setup
Fairness Audit Report with bias mitigation recommendations
Research Paper documenting methodology and findings

Make sure it works with a csv/excel table and add real peramiters that are adjustible to then train the model on for then to enter the grades the user got for the prediction all in 1 html file