Data Mining Practice (Financial Risk Control): Financial Risk Control Loan Default Prediction Challenge (Part 1) [xgboots/lightgbm/Catboost and other models]--model fusion: stacking, blending

[Introduction and Practice of Machine Learning] A must-see series for beginners, including actual data mining projects: data fusion, feature optimization, feature dimensionality reduction, exploratory analysis, etc. The actual combat will take you to master machine learning data mining

The column introduces in detail: 【Introduction and Practice of Machine Learning】Must-see series for introductory collections, including actual data mining projects: data fusion, feature optimization, feature dimensionality reduction, exploratory analysis, etc. The actual combat will take you to master machine learning data mining.

This column is mainly to facilitate beginners to quickly grasp relevant knowledge. Disclaimer: Some projects are online classic projects for everyone to learn quickly, and practical links will be added in the future (competitions, papers, practical applications, etc.)

Column Subscription: Data Mining - Machine Learning Column

It mainly explains the exploratory analysis of data: checking the correlation between variables and finding key variables; data feature engineering for data refinement: outlier processing, normalization processing, and feature dimensionality reduction; mainstream ML models involved in regression model training: decision-making Trees, random forests, lightgbm, Catboost, etc. At the same time, it focuses on model verification, feature optimization, model fusion, etc.

Data mining practice (financial risk control