O objetivo deste projeto é demonstrar como processar eficientemente um arquivo de dados massivo contendo 1 bilhão de linhas (~15GB), especificamente para calcular estatísticas (Incluindo agregação e ordenação que são operações pesadas) utilizando Python.
Traffic Flow Forecasting using FireDucks - Optimized data processing and AI modeling for accurate predictions.
A complete guide to ensuring seamless compatibility between pandas and cuDF in GPU-accelerated data processing. Covers data type handling, missing values, and merge operations step-by-step. Perfect for developers transitioning from pandas to FireDucks. 🚀
This project analyzes an online retail dataset using data cleaning, feature engineering, exploratory data analysis (EDA), and customer segmentation. It also optimizes performance using the Fireducks library for faster computations. Key insights include sales trends, customer behavior, and RFM-based segmentation to enhance business decisions.
Exploratory data analysis (EDA)
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