VinUni Datathon 2026: Practical Analysis of an End-to-End Data Science Competition Project

This article provides an in-depth analysis of the end-to-end practice of an excellent participating project in the VinUni 2026 Data Science Competition, covering data preprocessing, exploratory data analysis (EDA), machine learning model construction and optimization, engineering practice, and insights from competition experience, offering practical references for participants in data science competitions.

VinUni Datathon is an annual data science competition hosted by VinUniversity in Vietnam, aiming to provide students and data analysis enthusiasts with practical opportunities in real business scenarios. The 2026 competition required participants to complete the full process from raw data to a deployable model within a limited time. This article analyzes an excellent project in this competition and discusses its technology selection and implementation details.

Data preprocessing accounts for more than 60% of the project's workload. This project adopted a systematic cleaning process (missing value handling, outlier detection, data type conversion) and ensured consistent data distribution between the training and test sets through multi-stage verification to avoid model performance degradation. For categorical features, one-hot, target, and embedding encoding were tried; for numerical features, standardization, binning conversion, and polynomial feature generation were performed to capture non-linear relationships.

The project's EDA went from univariate analysis to multivariate relationship mining, revealing key business insights through visualization. It focused on feature correlation matrices to handle multicollinearity issues; discovered the skewed distribution of the target variable and adjusted evaluation metrics and loss functions; and mined hidden patterns through time series decomposition (if applicable) and spatial clustering to guide the direction of feature engineering.

Model selection adopted an ensemble learning approach, building a multi-model system of gradient boosting trees, random forests, and neural networks to balance model capacity and overfitting risk. Hyperparameter optimization used Bayesian optimization combined with cross-validation to efficiently explore the hyperparameter space; custom loss functions and evaluation metrics were implemented to align with business objectives; model fusion used Stacking and Blending techniques, with a meta-learner integrating base model prediction results to improve performance.

The project used a modular code structure, separating data processing, feature engineering, model training, and evaluation components; version control tools were used to manage code iterations and record complete experiment logs (hyperparameters, training time, performance metrics); detailed documentation and a requirements.txt file were provided to ensure result reproducibility, reflecting the importance of engineering thinking for team collaboration and knowledge inheritance.

Experience summarized from project practice: Deeply understanding the business and data background is more important than blind parameter tuning; systematic experiment management and version control are the foundation for efficient iteration; model performance improvement comes from data quality optimization rather than merely algorithm complexity. It is recommended that competition participants start with a baseline solution, gradually introduce innovative points, pay attention to the latest research but solidly master basic methodologies. Competitions are a comprehensive test of technology, problem-solving thinking, and engineering capabilities.