# LeetCode Rating Predictor: A Contest Score Prediction System Based on Deep Neural Networks

> A full-stack LeetCode contest score prediction application implemented using a dense neural network (TensorFlow) with 15-dimensional features, including a FastAPI backend and React frontend, supporting real-time prediction.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-10T04:24:13.000Z
- 最近活动: 2026-05-10T04:32:18.449Z
- 热度: 141.9
- 关键词: LeetCode, 评分预测, 深度学习, TensorFlow, FastAPI, React, 全栈开发, 神经网络
- 页面链接: https://www.zingnex.cn/en/forum/thread/leetcode-rating-predictor
- Canonical: https://www.zingnex.cn/forum/thread/leetcode-rating-predictor
- Markdown 来源: floors_fallback

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## LeetCode Rating Predictor Guide: A Full-Stack Contest Score Prediction Application Based on Deep Neural Networks

LeetCode Rating Predictor is a full-stack application for algorithm contest enthusiasts. It uses a dense neural network (TensorFlow) with 15-dimensional features to predict contest rating changes, including a FastAPI backend and React frontend, supporting real-time prediction. Target users include contest participants, algorithm learners, and tech enthusiasts, providing an intuitive prediction tool and learning reference.

## Project Background and Target Users

### Background
To provide a real-time rating prediction tool for contest participants, combining machine learning and web development practices to meet users' needs such as pre-contest assessment and progress tracking.
### Target Users
- **Contest Participants**: Assess pre-contest status and set goals
- **Algorithm Learners**: Track progress and analyze weak points
- **Tech Enthusiasts**: Learn full-stack ML application architecture and feature engineering

## Technical Architecture and Implementation Methods

### Backend: FastAPI + TensorFlow
- Asynchronous high-performance framework with auto-generated API documentation
- Data acquisition layer crawls user historical data with caching strategy
- 15-dimensional features: basic statistics, difficulty distribution, contest history, time series
- Model structure: Input layer → 64-unit ReLU → Dropout(0.3) → 32-unit ReLU → Dropout(0.2) → Linear output layer
### Frontend: React + Data Visualization
- React18 + TypeScript + Tailwind CSS + Recharts
- Core components: Search, Dashboard (trend chart/radar chart), Prediction results
- Responsive design supporting mobile devices and dark mode

## Model Training and Performance Evaluation

### Data Processing
- Sources: LeetCode API, anonymized user data, historical contest archives
- Process: Cleaning → Feature calculation → Standardization → Time window splitting
### Training Configuration
- Optimizer Adam(0.001), loss MSE, evaluation metrics MAE/RMSE/R²
- Early stopping mechanism to prevent overfitting
### Performance
- Offline test set RMSE ~45 points, R² ~0.72
- Online evaluation collects real feedback and conducts A/B testing

## Project Summary and Core Contributions

LeetCode Rating Predictor is an excellent full-stack ML project that demonstrates the practical value of combining deep learning and web technologies. Core contributions:
1. Provides a reference for the complete ML application development process
2. Reflects the importance of feature engineering in real-world problems
3. Balances model performance and engineering complexity
It provides a progress tracking tool for contest enthusiasts and a practical case for tech learners.

## Limitations and Future Improvement Directions

### Current Limitations
- Data relies on LeetCode API, insufficient data for new users
- Large prediction deviation in extreme cases, insufficient function interpretability
### Future Improvements
- Model upgrade: LSTM/Transformer, graph neural networks
- Function expansion: Recommendation system, learning path planning
- Engineering optimization: Feature platform, model serving (MLflow/Seldon)