data_analysis The code in the directory is not organized at all. Please be patient while preparing... For Data Mining 0. EDA Visualize Feature Importance(gain, split)/Feature_Permutation/Partial Dependence Plot correlation Adversarial Distribusion 1. Preprocessing Outlier -Construciton Impute -Impute by BaseAggregation -Impute by Regression(LGBM) Replace 2. Feature Engineering Categorical Encoding -Likelihood Encoding(Target Encoding) -Count Encoding -One Hot Encoding -Label Encoding Base Aggregation Dimentional Reduction(Embedding) -PCA -LDA -tSNE -UMAP Clustering -Kmeans -EM Algorithm 3. Feature Selection Feature Importance (LGBM) Feature Permutation 4. Feature Management 5. Various Machine Learning Algorithm 6. Explainer Shap LIME(Unexecuted) 99. Important These mean & std is same.
