Up to 90% accuracy with just 5 features using KNN algorithm and PCA for feature engineering. The dataset contained less than 1000 observations. The model's accuracy could be improved using more observations, further hyperparameter optimization and feature engineering

Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs

Assignments of Data-Science(COSE471 김진규)

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Cloud-based AI / ML workflow and data application development framework

Docs for Chalk AI

An open source python library for automated feature engineering

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

CANtropy: Time Series Feature Extraction-Based Intrusion Detection Systems for Controller Area Networks

A Gradio machine learning application that predicts Danish car prices. Project includes web scraping, feature engineering, model creation and deployment

OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.

Using PostgreSQL for data analysis

IbisML is a library for building scalable ML pipelines using Ibis.

practicing some basic pandas, pyplot and plotly routines

EvalML is an AutoML library written in python.

* Basis EDA * Handling Null/Missing Values * Handling Outliers * Handling Skewness * Handling Categorical Features * Data Normalization and Scaling * Feature Engineering

首届社交群体智能算法大赛 【赛题1：社交媒体舆论场虚假账号检测】第三名(0.8248)方案

IU Projects