Akashdip Saha

Siliguri.

About

Work

Infosys Springboard
|

Data Science Intern

Highlights

Worked in teams with daily sprints, meetings, and evaluations guided by a mentor.

Completed a project on Unsupervised Anomaly Detection in Healthcare Providers' Data using ML and DL techniques.

Achieved high model performance and deployed a Flask-based application for real-time anomaly detection.

KaroStartup
|

UI/UX Intern

Highlights

As part of the evaluation process, designed a food delivery gamified section in the app, enabling users to play games while earning rewards, creating mutual benefits for users and the company.

Collaborated with the UI/UX team to create responsive Figma prototypes for app registration and landing pages.

Celebal Technologies
|

Data Science Intern

Highlights

Analyzed credit risk (P1, P2, P3, & P4) using XGBoost, achieving an accuracy score of 82% after tuning.

Initially, Random Forest and Decision Tree models achieved 76% and 71% accuracy, respectively.

Education

Kalinga Institute of Industrial Technology, Odisha

BTech

Computer Science

Grade: 7.96

Awards

Amazon ML Summer School

Awarded By

Amazon

Selected among the top 3k out of 81k+ applicants for the Amazon Machine Learning Summer School 2024, gaining insights into advanced ML topics through sessions with Amazon applied scientists.

IEEE AESPC-2024 Paper Presentation Certificate

Awarded By

IEEE

Presented the paper "Leveraging Advanced Deep Learning Techniques for Automated Dermatological Image Classification" at IEEE AESPC-2024, highlighting AI applications in healthcare.

Certificates

Deep Learning Training

Issued By

Internshala

Salesforce Developer Virtual Intern

Issued By

SmartInternz

Skills

Programming Languages & Technologies

C++ (including OOP concepts), Python libraries (ML and data analysis), AI/ML, UI/UX Design (Figma), Frontend Development (HTML, CSS), DBMS, SQL, Cloud Computing, Computer Networks, Operating Systems.

Projects

Unsupervised Anomaly Detection on Healthcare Providers' Data
Skin Cancer Lesion Multi-Classification System