Venkatesh Vemani

Experienced Senior Analyst with 2.5+ years in the software industry, skilled in developing test plans, executing various testing methodologies, and automating tasks using Python and JavaScript. Proficient in Agile/Scrum methodologies and certified in Google Data Analytics, IBM Data Scientist, and Microsoft Azure Fundamentals. Strong in Python, SQL, R Studio, and Power BI for data analysis. Effective communicator, collaborator, and problem solver committed to continuous learning and team success.

Flight Fare Prediction

Developed predictive models using Linear Regression and Random Forest algorithms to estimate flight fares, helping travelers make informed booking decisions. Enhanced model accuracy through data pre-processing, visualization, and analysis, while also staying updated on emerging trends through active participation in a seminar focused on flight fare prediction, fostering professional growth within the domain. Automobile Risk Analysis

Utilized various machine learning techniques including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting to analyze car safety factors, providing valuable insights to stakeholders in the automotive industry. Conducted detailed analysis beyond traditional considerations, identifying nuanced factors impacting automobile risk and enabling informed decision-making and the development of robust risk mitigation strategies. Additionally, actively engaged in a seminar focused on car safety analysis, enhancing expertise, and staying abreast of emerging trends to foster professional growth in automotive safety. 

Effect of Smoking on Plasma Retinol Level 

Applied various statistical methods including ANOVA, Fisher, Welch, and Kruskal-Wallis to scrutinize the influence of smoke on plasma retinol levels, ensuring a comprehensive examination of the data. Performed post-hoc analyses using Wilcoxon and pairwise t-tests with Bonferroni and Holm corrections to further investigate significant findings, enhancing the understanding of observed effects and contributing significantly to data analysis and interpretation. Additionally, presented project findings comprehensively in a seminar, effectively communicating statistical results to stakeholders.