Shivam Baluni

Developed predictive models for telecom churn using Python, leveraging machine learning algorithms and statistical analysis to identify key factors influencing customer attrition. Conducted exploratory data analysis (EDA) on large datasets to extract meaningful insights. Implemented several machine learning models to predict churn, and performed model comparison for best results. Achieved an accuracy of 92% on test and train data. Proficient in AWS cloud services gained through coursework.

Completed intensive data analysis coursework at ExcelR, focusing on Python and machine learning. Proficient in Python for data manipulation, analysis, and modelling using Pandas, NumPy, and Scikit-Learn. Developed and deployed predictive models for regression, classification, and recommendation tasks.

Numerical simulation of fluidized bed using OpenFoam and MFiX. Data preparation and graphical representation for publications using Python. Data presentation in international conferences was performed using Python visualisation library like Seaborn, Plotly, Matplotlib.

Numerical Simulation and development of a tool for melt pool size calculation in Python and VBA. Result compilation and data representation was performed using Tableau. Development of an excel tool using VBA to solve the heat conduction problems.

Developing a coupled simulation model to combine filling simulation and structural tool simulation for injection molding. Development of a framework for coupled simulation using Moldex3D and Ansys. Collection and study of simulation data for comparison using Pandas in Python.

Lead a team to develop the Vendor Management Tool, which improved vendor performance and data management for Amazon.de. Provided training and support to the vendors for preparing amazon.de catalogue. Conducting knowledge transfer sessions to the new users regarding the internal tools and database.