The objective of this project was to analyze and gain insights from the US road accident from 2016 to 2022 data to better understand the contributing factors, patterns, and trends associated with accidents. By examining the dataset, we aim to uncover valuable information that can help in developing strategies to reduce the number of accidents and improve road safety.

December 2023   •   Code   •   report

This project aimed to analyze and extract insights from US road accident data spanning 2016-2022.I focused on examining California as the top state for road accidents within the United States. Through comprehensive analysis, it aimed to uncover the primary factors contributing to California's high accident rates compared to other states, identifying patterns and trends to inform targeted strategies for accident prevention and improving overall road safety.

Nov 2023   •   Code   •   report

Performed time series analysis on 500+ time-series meter data from buildings data genome project. Utilized K-means Clustering on electrical meter data to identify daily load profiles and implemented a k-nearest neighbor regression model to accurately predict energy consumption with a MAPE of 6.59%.

December 2022   •   Code

I developed two interactive Tableau dashboards as part of my projects. One dashboard focused on Netflix movie analytics, offering insights into viewership trends, ratings, and popular genres. The other dashboard tracked retail sales data for a bicycle company operating in Australia, providing detailed analysis of sales performance, geographical distribution, and product trends.

Nov 2023   •   Netflix   •   Bicycle Sales

Quantitative risk assesment associated with electric vehicle batteries using fault tree and event tree analysis, to identify potential failure modes and their probabilities, highlighting risks of component failure due to overheating. Performance risk assessment use cyclic life testing data to assess battery longevity and failure rates.key finding include the risk value of top event is only 8.19%. Reliability modeling using Weibull distribution and uncertainty analysis estimates mean time to failure (MTTF) of 13,080 hrs.

December 2022   •   Report

This project optimizes Mareana Turbine's production lines, identifying bottlenecks at QA and CNC stations through Flexsim simulation. Proposed enhancements include additional workstations for key impeller lines and adopting Industry 4.0 technologies like predictive maintenance and AI inspection to boost efficiency. The strategy aims to meet demand, increase revenue by $700K, and incorporate digital advancements at a $150K cost.

December 2022   •   Report