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This project is an in-depth analysis of Transport for London (TfL) passenger footfall data, submitted as the final coursework for the MTH6139 Time Series module at Queen Mary University of London. The primary goal was to apply statistical forecasting techniques to model historical trends and predict future passenger numbers.

Homework 1IE4331

Structural VAR models on Output, Unemployment, Money Supply and inflation in South Africa, where the money supply is represented by a discretised Divisia (törnqvist) index.

enerapan algoritma Naïve Bayes menggunakan bahasa pemrograman R untuk memprediksi variabel Reengagement. Proses analisis meliputi tahap pembersihan data, pembagian data menjadi training set dan testing set, serta penerapan metode resampling untuk mengatasi ketidakseimbangan data. Hasil evaluasi model ditampilkan dalam bentuk confusion matrix dan metrik performa (akurasi, sensitivitas, spesifisitas, dan balanced accuracy).

This webpage presents the Peer-graded Assignment: R Markdown and Leaflet

A Structural VectorAutoregression of real GDP, Unemployment, inflation and money supply (monetary policy)

Jourast Buwana

In this project, I will analyze RNA-seq data from the brain of a 90-year-old woman in the RUSH Alzheimer’s study. The dataset includes information on gene length, FPKM (Fragments Per Kilobase of transcript per Million mapped reads), and TPM (Transcripts Per Million). I will create 3 exploratory plots that give biologically relevant insights into gene expression in this tissue.