MWENDE-CURTIS/Kenya_2007_Post_Election_Impact_Analysis

This project analyzes key national time-series data to statistically quantify the economic and social shock caused by the 2007 post-election violence in Kenya. We will use regression and time-series models across Python, R, and Julia to estimate the magnitude, duration, and subsequent recovery period of this significant non-economic event. Statistical Computing Group Project: Quantifying the Post-2007 Election Impact on Kenya Group Member Details This project was developed by the following five-member team: VALERY MWENDE MWITI 0743447143 MARK GICHUHI 0712098136 SARAH KAGIA 0708514673 KEITH MUREGA 0707451296 KUNTAI NATHAN 0757680399 SHARON ATABO 0703668617 Repository Structure README.md Project overview and team information. folder_one Data Acquisition and Exploratory Data Analysis (EDA) in Python. folder_two Advanced Statistical Analysis and Visualizations in R. folder_three Content for Presentation 3: Modeling, Simulation, and Performance Metrics in Julia. Project Overview: Quantifying the Post-2007 Election Impact on Kenya This project conducts a rigorous statistical analysis using time-series data to quantify the socio-economic shock following the 2007 Kenyan General Elections. The study aims to estimate the precise magnitude and duration of the regression in key national indicators, such as Quarterly GDP Growth and Tourism Revenue. By utilizing a three-language approach, we demonstrate mastery of statistical computing workflows: Python (folder_one): Used for efficient Data Acquisition, Cleaning, and Exploratory Data Analysis (EDA). R (folder_two): Used for Advanced Statistical Modeling (e.g., ARIMA) and generating high-quality, interpretive visualizations. Julia (folder_three): Used for Computational Benchmarking and High-Performance Simulation to model the post-shock recovery dynamics. The findings provide crucial data-driven insights for risk assessment and policy planning for future periods of potential political instability in Kenya.