Vidya Ganesan

Qualified Data Science Intern with solid foundation in statistical analysis and machine learning. Contributed to predictive modeling projects and data visualization tasks in previous internship, demonstrating practical applications of theoretical knowledge. Proficient in Python and R, with keen ability to interpret complex data sets and collaborate effectively within team environments.

• Adjusting Geometric-Arithmetic Index to Estimate Boiling Point, 2020, https://match.pmf.kg.ac.rs/electronic_versions/Match84/n2/match84n2_483-497.pdf
• Strong Chromatic Index of Certain Nanosheets, 2017, https://jmathnano.sru.ac.ir/article_703.html
• Strong Edge-Coloring of Sierpinski like Graphs, 2016, https://acadpubl.eu/jsi/2016-109-si/9/21.pdf

• Thesis - Variants of Edge Coloring in Networks, The thesis elaborates on the concepts of graph theory and interconnection networks which are widely used to solve many real-time applications., Briefly explain the various edge coloring problems and obtained sharp bounds for some networks., Introduced a new variant of edge coloring problem known as induced edge coloring and obtained a bound. Obtained NP-complete for certain problems.
• Project - Prediction of Diabetes, It is to build a machine learning model on predicting whether a patient has diabetes or not based on a diagnosis., Performed descriptive analysis to understand the variable and their corresponding values., The main objective is to train the dataset on various models and to choose the best fit. Created a classification report by analyzing accuracy and precision.
• Project - Lending Club Loan Data Analysis, The primary goal of the problem is to predict the chance of default for future loans., To overcome the effects of a highly imbalanced dataset, sampling techniques are applied., Performed EDA of different factors of the dataset and feature engineering is performed. Machine learning and deep learning models are implemented.