Lccn_predictor

Overview

This product is a Leetcode weekly and biweekly contest rating predictor. It is an online application that provides predicted results for these contests. The core prediction algorithm is fast and accurate, providing users with their predicted ratings within 20-30 minutes after the contest has finished. The web page is also responsive and can be accessed on phones and tablets. The product utilizes various technologies and frameworks such as MongoDB, Beanie, APScheduler, Numpy, Numba, FastAPI, React, TailwindCSS, DaisyUI, and Echarts.

Features

• Fast: The core prediction algorithm is accelerated by a JIT compiler (Numba), resulting in a prediction time of around 20 seconds. The user’s latest rating is cached multiple times before predicting, saving time in fetching data.
• Accurate: The rating difference should be negligible if there are no massive rejudges. Even in the case of misconduct detection, the user’s global ranking will be slightly higher, resulting in a slightly higher rating.
• Responsive web page: The web page has been tested and optimized for phones and tablets.

Installation

To install this Leetcode weekly and biweekly contest rating predictor, follow the steps below:

1. Clone the repository: git clone [repository url]
2. Create a virtual environment: virtualenv venv
3. Activate the virtual environment:
   • For Windows: venv\Scripts\activate
   • For Unix/Linux: source venv/bin/activate
4. Install the required dependencies: pip install -r requirements.txt
5. Run the backend server: python main.py
6. Open the frontend directory: cd frontend
7. Install the frontend dependencies: npm install
8. Run the frontend server: npm start

Summary

This Leetcode weekly and biweekly contest rating predictor is a fast and accurate application that predicts users’ ratings for these contests. It utilizes various technologies and frameworks to provide a responsive web page and efficient prediction algorithm. With its installation guide, users can easily set it up and start using it to get their predicted ratings.