Sibimanyu Muralidharan

Student with a strong engineering background and excellent coding and machine learning skills. Motivated by the opportunity to leverage my technical acumen in the field of computer science with a particular interest in coding, data science, and machine learning. Committed to working in a learning and challenging environment. Adept at analytical thinking, and building strong client- facing relationships. Capable of working independently and collaboratively on projects, and committed to achieving team goals.

Part of Siruthuli- NGO based out in Coimbatore, active member in volunteering. Entrepreneur's Club and English Literary Society Club, PSGCT, was an active member in organizing events, designed posters and lanyard.

Target Retail - SQL Project

https://github.com/greatape17377/Portfolio_Projects

Analyzed SQL data to comprehend a business case study encompassing over 100,000 orders placed by Target Brazil from 2016 to 2018.

Executed SQL queries to uncover patterns, trends, and statistics within the dataset. Extracted valuable insights and formulaterecommendations through in-depth data analysis, utilizing multiple attributes to draw conclusions.

COVID-19 SQL Exploration

https://github.com/greatape17377/COVID19_SQL-EXPLORATION

Utilized the percentage of death per Country/Region for insightful analysis, contributing to a 15% increase in understanding mortality patterns.

Streamlined data extraction processes through SQL queries in BigQuery, resulting in a 30% reduction in query response time.

Visualized Shortest Paths/Routes in PowerBI

Extracted meaningful insights and formulated actionable recommendations by leveraging the power of the dashboard. Analyzed the visualized data to deliver valuable information to end customers.

Executed SQL queries to retrieve data from the backend database, systematically calculating the shortest paths from airports to hotels, petrol pumps, and shops.

Achieved a 15% reduction in travel time and expenses through the execution of optimized route recommendations.

Intelligent Character Recognition System

Implemented Supervised Machine Learning techniques, specifically SVM and HMM, for deploying handwritten character recognition in the Tamil language.

Evaluated and compared multiple Machine Learning algorithms, fine-tuning datasets to enhance the learning process.

Achieved a commendable recognition accuracy of 76% with the proposed system

• Best Paper Award, Published a research paper in ML - Intelligent Character Recognition System for Text-to-Speech Synthesis System.
• Best Outgoing Project Award, PSGCT.