🏃‍♀️ Process, Visualize, and Analyze Actigraphy Data

This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.

A PySpark-based solution for cleaning and interpolating battery sensor data using forward/backward fill and Radial Basis Function (RBF) spatial interpolation. Outputs a clean, fully interpolated dataset in CSV format for advanced analysis.

Java/Spring Boot + Swing desktop app for cleaning and interpolating time-series CSV data. Automatically detects intervals, fills missing timestamps, preserves keywords, computes per-column statistics, and uploads cleaned files to AWS S3. Supports large datasets (800+ columns, 120+ hours).

A Brazil climate estimate cluster analysis from 11 year INMET spatio-temporal meteorological data.