Sankari Chandrasekaran

I am a highly skilled Data Scientist with a strong academic foundation in Graph Theory (PhD) and a Master's in Data Science & Machine Learning. I bring expertise in statistical analysis, predictive modeling, and data visualization, combining my advanced mathematical knowledge with hands-on experience in machine learning. I have delivered impactful results in previous roles, including internships, where I contributed to predictive modeling and data visualization tasks. I am familiar with key libraries such as Pandas, NumPy, Scikit-learn, NLTK, Matplotlib, and Seaborn, and I am also highly adaptable and thrive in dynamic environments. As a strong collaborator, I am skilled in Python and equipped to tackle complex data-driven challenges.

• Life Member of ADMA (Academy of Discrete Mathematics and Applications)
• Life Member of IMS (Indian Mathematical Society)

• Decompositions of the complete graphs into paths and cycles of distinct lengths and Kneser graphs into stars.
• Claw-Decomposition of Kneser Graphs, C. Sankari, R. Sangeetha, K. Arthi, Trans. Comb., 11, 1, 2022, 53-61, WEB OF SCIENCE-ESCI
• Decomposition of the Complete graphs into paths and cycles of distinct lengths, C. Sankari, R. Sangeetha, K. Arthi, Punjab Univ. J. math., 55, 5-6, 2023, 197-204, WEB OF SCIENCE-ESCI
• S5-decomposition of Kneser Graphs, C. Sankari, R. Sangeetha, K. Arthi, South East Asian J. of Mathematics and Mathematical Sciences, 18, 2, 2022, 171-184, SCOPUS
• {Cn,C4}-Decomposition of the line graph of the complete graph, K. Arthi, C. Sankari, R. Sangeetha, TWMS J. App. and Eng. Math., 12, 4, 2022, 1441-1447, WEB OF SCIENCE-ESCI
• Decomposition of the Complete Graph into Paths and Cycles of Distinct Lengths-II, K. Arthi, C. Sankari, R. Sangeetha, Indian J. Discrete Math., 5, 2, 2019, 99-109, UGC-CARE
• A Note on {Cn, S4}-Decomposition of L(Kn), C. Sankari, K. Arthi, R. Sangeetha, Indian J. Discrete Math., 5, 2, 2019, 95-98, UGC-CARE
• S4-Decomposition of the line graph of the complete graph, K. Arthi, R. Sangeetha, C. Sankari, Serdica Math. J., 47, 4, 2021, 273-284, UGC-CARE
• S5-Decomposition of the line graph of the complete graph, K. Arthi, R. Sangeetha, C. Sankari, Proceedings of an 18th Annual Conference of ADMA and Graph Theory Day, 06/09-06/11/22, Vellalar College for Women, Erode, 9, 2, 2023, 73-80, UGC-CARE
• Claw-Decomposition of Generalized Kneser Graph GKGn,3,1, K. Arthi, R. Sangeetha, C. Sankari, submitted
• Claw-decomposition of Kneser Graph KGn;3, C. Sankari, R. Sangeetha, K. Arthi, accepted, TWMS Journal of Applied and Engineering Mathematics, WEB OF SCIENCE-ESCI

Decompositions of Graphs, Graph Algorithms, Machine Learning Techniques, Data Science

Git Hub Link:

• Diabetes Data Model Prediction:

This project uses a diabetes dataset to predict whether a patient has diabetes or not. As a classification problem, I developed the model using the Support Vector Classifier (SVC) algorithm to effectively classify patients based on various health-related features. 

 Git hub link: https://github.com/Sankari9791/Project__1_Diabetes_Data

• Sonar Data Model Prediction:

This project uses the Sonar dataset to predict whether the object is rock or metal. As a classification problem, I developed the model using Logistic Regression to classify the objects based on sonar signals.

Git hub link: https://github.com/Sankari9791/Project_2_Sonar_Data

Now, I am doing my internship in Twilearn EduTech. I am working on the project on "Customer_Churn_Details"

• This project uses the Customer Churn dataset to predict whether customers will churn or not. I developed the model using Logistic Regression to classify customers based on various factors that contribute to churn.

Git Hub Link: https://github.com/Sankari9791/Churn_details