ml-data-prep-eval

name

ml-data-prep-eval

description

Prepare and evaluate machine learning data. Use this skill whenever the user needs to clean, transform, or split datasets for ML training, or evaluate model performance with metrics like accuracy, precision, recall, F1, ROC-AUC, MAE, or confusion matrices. Trigger for any data preprocessing task, feature engineering, handling missing values, encoding categorical variables, normalization, or model evaluation requests.

ML Data Preparation & Evaluation

This skill helps you prepare raw data for machine learning and evaluate model performance. Follow the workflow below for systematic data preparation.

Quick Start

# Clean and prepare your data
python scripts/data_cleaning.py --input data.csv --output cleaned_data.csv

# Transform features
python scripts/data_transformation.py --input cleaned_data.csv --output transformed_data.csv

# Split for training
python scripts/data_splitting.py --input transformed_data.csv --train-ratio 0.7 --val-ratio 0.15

# Evaluate model predictions
python scripts/model_evaluation.py --actual actual.csv --predicted predictions.csv

Workflow Overview

1. Data Collection → Gather from databases, APIs, files, or web scraping
2. Data Cleaning → Handle missing values, remove duplicates, filter outliers
3. Data Transformation → Normalize, encode, engineer features
4. Data Splitting → Create train/validation/test sets
5. Model Evaluation → Calculate performance metrics

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1. Data Collection

Supported Sources

Source	Method	Example