M.Tech-Chemical Engineering Indian Institute of Technology, Kanpur
Student Guide
Summary
guiding newly admitted students and assisting in ID card creation.
Teaching Assistant
Summary
Mass Transfer and Separation Processes CHE213, Dept of ChE
→
M.Tech
Chemical Engineering
Grade: 9.37/10
→
B.Tech
Chemical Engineering
Grade: 7.92/10
→
Class XII
Class XII
Grade: 96.3
→
Class X
Class X
Grade: 9.5/10
Awarded By
GATE
Awarded By
JEE Advanced
Published by
Prof.Pankaj Arvind Apte
Summary
Conducted an extensive literature survey on Carbon dioxide methanation, identifying key research gaps and proposing crucial areas for future investigation to advance the field. Implementing sensitivity analysis to identify key reaction mechanisms and rate-determining steps that significantly impact overall reaction rates and product selectivity in Carbon-dioxide methanation.
Issued By
Udemy
Issued By
Udemy
Issued By
Edureka
Issued By
CodeIn Academy, Udemy
Issued By
LeetCode
Issued By
Hacker rank
Python, Java, C++, SQL.
HTML, CSS, Bootstrap, JavaScript, React.js.
Pandas, Scikit-learn, Matplotlib, NumPy.
Oracle Database, MS Excel, LaTeX.
Data Structures and Algorithms, Machine Learning, Problem-Solving.
Summary
Carried out a thorough SQL analysis on pizza sales data, using INNER JOIN, sub-queries, GROUP BY clauses, and aggregation functions across 4 tables. Developed SQL queries to calculate key perfromance indicators (KPIs) like total revenue, orders, and more.
Summary
Developed a food delivery website using the MERN stack, featuring a user interface for login, ordering, and payment processing with React, and an admin interface for managing food items, including adding or deleting items. Leveraged React hooks (useState, useEffect, useContext), Axios for data fetching, react-router-dom for routing, Express.js for backend APIs, and MongoDB for managing user data, food items, and transactions.
Summary
Processed a 13,000-row dataset with data cleaning, outlier removal, and one-hot encoding of categorical variables. Developed and evaluated predictive models, achieving an R² score of 0.818 with Linear Regression.
Summary
Created an ML model to detect the authenticity of credit card transactions from a dataset of 280K+ datapoints. Optimized model performance through hyperparameter tuning and cross-validation, achieving a recall score of 0.9302 with a Neural Network, outperforming other ML models.