Biswas Kumar
Data analyst | Data scientist
Areas of Interest
I want to help others to make the right decisions, data-driven decisions. My interest lies in machine learning, supervised and unsupervised algorithms, and the use of data visualization techniques to present the results. I use SQL, R, Python, and PowerBI for data projects.
Data Analysis
I love telling a story. Getting to the heart of a problem and coming up with an optimal solution.
Machine Learning
I am passionate about learning the theory that is pushing the cutting edge of Machine Learning.
Dashboards
I enjoy building interactive dashboards extensively in projects to highlight and analyse performances of KPIs.
Articles and Reports
Passionate about crafting engaging reports. One of my marketing articles featured in a UN-backed magazine.
About me
Hi, my name is Biswas Kumar and I am a Data Science professional. I hold a Master's degree in Statistics & Machine Learning (Linköping University, Sweden) and an MBA in Marketing (Pune University, India).
I love to play with data and try to find meaningful pattern out of it. I have been working in data analytics for over eight years with experiance in energy and infrastructure sector. I have diversified background and experiance which gives me the ability to translate complex questions into understandable insights.
Do you want to work together? Please reach out to me by email or connect on LinkedIn.
My Recent Projects
Classification Models for Activity Recognition using Smart Phone Accelerometers
Developed and evaluated different Machine learning models using real-world smartphone-based tri-axial accelerometers labeled data to perform six activity recognition tasks. The published report was downloaded over 175 times!
Diabetes Prediction Web App using Machine Learning algorithms
Developed a Machine Learning based web app for prediction of diabetes. Visitors on web app can slide parameters for their diabetes prediction.
Optimal designs for sub-regions’ effects in multi-environment crop variety
Studied and researched about optimal design for sub-region effects on multi-environment crop variety testing using hierarchical linear mixed model.