FeatureEngineering
A clean and modular Python toolkit for feature engineering, including data preprocessing, transformation, encoding, scaling and feature selection. Suitable for both exploratory data analysis and production workflows.
Install / Use
/learn @FinDii/FeatureEngineeringREADME
<h1 align="center">🔧 Feature Engineering Toolkit (Python)</h1>
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A modular and reusable toolkit for performing feature engineering on structured datasets.<br>
This repository provides essential utilities for preprocessing, transforming, and optimizing features for quant finance and machine learning workflows.
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📦 Overview
Feature engineering is one of the most critical steps in quant projects.
Here provides a clean pipeline and practical examples for:
- 🧹 Data preprocessing：missing value handling, outlier detection
- 📊 Exploratory Feature Analysis：time series analysis, classical technical indices, correlation, visual comparison
- 🔣 Feature Transformation：temporal dimension, Cross-sectional features, interaction, contextual dimension, demensional reduction
- 📐 Advanced Engineering：improved engineering methods based on the previous results and comparisons
- ✂️ Feature Selection：selection based on correlation changes, SHAP from Catboost models
This repository can serve both as a reference and a reusable feature engineering module.
📁 Project Structure
- Data used: data_cp.csv (too big for uploading)
- Notebooks:
- Technical Indices.ipynb
- TimeSeriesAnalysis.ipynb
- FeatureEngeering_basic.ipynb
