Mapa de la intensitat d'observacions del GBIF (Global Biodiversity Information Facility) de les espècies de singnàtids trobades al mar Mediterrani.

Türkiye, Meksika ve Brezilya büyüme dinamikleri analizi (2000-2020).

The project aims to utilize a particular crop related dataset (Paddy / Rice Dataset from UC Irvene Machine Learning repository utilized in current case), which contains multiple agronomic, environmental, and crop‑related features, for the purpose of dimension reduction. Modern agricultural research increasingly relies on large, feature‑rich datasets to understand crop performance, optimize cultivation practices, and support data‑driven decision‑making. As farming conditions, climate patterns, and crop varieties evolve, the volume and complexity of agricultural yield continues to grow which is an expected practice. For current project, we utilize the full Paddy Dataset because all feature groups—soil characteristics, climate variables, crop breed or traits, and management practices—contribute to understanding paddy or rice growth patterns. Small variations across a few selective features can significantly influence yield, making dimension reduction a valuable tool for uncovering underlying structure in the dataset. And, accordingly the results can be utilized to harness parameters which influence paddy production volume the most for real world cultivation suggestions.

Tugas ini disusun sebagai salah satu syarat pemenuhan Ujian Akhir Semester (UAS) mata kuliah Komputasi Statistika 2

WQD7004 Programming for Data Science-Group Project-Group 12 member： LI YONGQI 25060741 LU YUXUAN 25068337 LILUANDONG 25066043 MIXIN 24060896 ZHANG TINGTING 25071760

The goal of this project is to build a predictive text engine. This report demonstrates the initial exploratory analysis of the training data, identifies major features of the language datasets, and outlines the plan for the final Shiny application.

This is the Economic Trend Analysis Visualisation of Economics data from the ggplot2 package of RStudio. Using Economics data of the USA, I analyse the Us Unemployment trend from 1967 to 2015, predict the personal savings rate of US citizens, and using Time series forecasting, I forecast the unemployment trend in the US. After analysis I combined the seasonal pattern + Trend + Residuals, I derived the Trend composition and decomposed the unemployment time series.