https://galliard7.github.io/tags/tabular/Recent content in Tabular on Galliard7Hugoen-usThu, 15 Jun 2023 00:00:00 +0000https://galliard7.github.io/projects/amex-default-prediction/Thu, 15 Jun 2023 00:00:00 +0000https://galliard7.github.io/projects/amex-default-prediction/<h2 id="overview">Overview</h2> <p>Predicting credit card default probability for American Express customers using anonymized transaction and account features. A large-scale tabular competition with heavy feature engineering requirements.</p> <h2 id="approach">Approach</h2> <ul> <li>Extensive feature engineering over time-series transaction histories</li> <li>Gradient boosting models (LightGBM, XGBoost, CatBoost)</li> <li>Aggregation features: rolling statistics, lag features, trend indicators</li> <li>Careful handling of missing values and categorical encodings</li> </ul> <h2 id="result">Result</h2> <p>287/4874 🥉</p> <h2 id="links">Links</h2> <ul> <li><a href="https://github.com/Galliard7/amex-default-prediction">GitHub Repository</a></li> </ul>https://galliard7.github.io/projects/jane-street-market-prediction/Wed, 15 Mar 2023 00:00:00 +0000https://galliard7.github.io/projects/jane-street-market-prediction/<h2 id="overview">Overview</h2> <p>Predicting profitable trading opportunities from anonymized financial market data. The competition required building models that could identify actionable signals in noisy, high-dimensional market features.</p> <h2 id="approach">Approach</h2> <ul> <li>Feature selection and denoising on anonymized market signals</li> <li>Gradient boosting and neural network ensembles</li> <li>Custom utility-based optimization aligned with competition metric</li> <li>Time-aware validation to avoid lookahead bias</li> </ul> <h2 id="links">Links</h2> <ul> <li><a href="https://github.com/Galliard7/jane-street-market-prediction">GitHub Repository</a></li> </ul>