Design Patterns MCP Server

{ "id": "data-preprocessing", "name": "Data Preprocessing", "category": "Machine Learning", "description": "Techniques for cleaning, transforming, and preparing raw data for machine learning models", "when_to_use": "Raw data with noise or missing values\nInconsistent data formats\nNeed for feature scaling or normalization\nData integration from multiple sources", "benefits": "Improved model accuracy\nReduced training time\nBetter generalization\nCleaner data insights", "drawbacks": "Can be time-consuming\nMay lose information\nRequires domain knowledge", "use_cases": "Feature engineering\nData cleaning pipelines\nETL processes\nMachine learning workflows", "complexity": "Medium", "tags": [ "machine-learning", "data-cleaning", "feature-engineering", "etl" ], "examples": { "typescript": { "language": "typescript", "code": "interface DataPreprocessor {\n clean(data: any[]): any[];\n normalize(data: any[]): any[];\n handleMissingValues(data: any[]): any[];\n}\n\nclass MLDataPreprocessor implements DataPreprocessor {\n clean(data: any[]): any[] {\n // Remove duplicates, outliers\n return data.filter(item => this.isValid(item));\n }\n\n normalize(data: any[]): any[] {\n // Min-max scaling or z-score normalization\n return data.map(item => this.scaleFeatures(item));\n }\n\n handleMissingValues(data: any[]): any[] {\n // Impute or remove missing values\n return data.map(item => this.imputeMissing(item));\n }\n\n private isValid(item: any): boolean {\n return item && typeof item === 'object';\n }\n\n private scaleFeatures(item: any): any {\n // Normalization logic\n return item;\n }\n\n private imputeMissing(item: any): any {\n // Imputation logic\n return item;\n }\n}" } } }