I'm a passionate CS Graduate student at the University of Southern California with a robust foundation in Data Science and Machine Learning. My academic and professional ambition is fueled by my passion for changing the world through technological innovation. As I work towards my graduate degree, I am eagerly seeking full-time opportunities starting May 2025 that align with my aspirations in a data-centric role (Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence). Let's connect and explore the possibilities together!
- Developed a forecasting model to analyze Esri Community health and forecast user behavior with 90% accuracy using Python-based regression algorithm and Excel, enhancing quarterly trend tracking and annual business project roadmap planning.
- Optimized the retention model using Khoros API backend and advanced data visualization and statistical data analysis to identify friction points for Esri Community members, thereby helping the team build actionable workflows to increase member retention.
- Compiled dynamic ad-hoc data intelligence insight reports for team directors and created automated data filtering pipelines for quicker reporting, which resulted in improved business agility, better internal collaboration & increased overall team productivity.
- Processed user engagement through data-driven analytics of 75,000+ students by leveraging SQL, Python, and its library eco-system to re-design old stale products and draft adaptive marketing strategies, resulting in 40% increase in organic website traffic.
- Supported development of a dedicated performance dashboard to identify key customer trends and interests, aiding content team in designing lucrative events and courses for students, contributing to a 30% boost in acquired customers and 20% in revenue.
- Launched a global business competition fostering entrepreneurship with 20,000+ high-school participants, leading a team of 40 interns that managed end-to-end project tech integration with Google Suite, Zoom, Mass Emailing Systems, etc.
- Created a data screening portal to track applications, read patterns, and optimize outreach to diverse demographics using Django and MySQL, enabling ease of use and streamlining evaluation process for 200+ industry leading judges & mentors
- Actively spearheaded and collaborated on a broad spectrum of humanitarian projects. These included tree plantation drives to combat deforestation, vaccination camps to promote public health, sanitation initiatives to improve community hygiene, educational programs for underprivileged youth, and food distribution efforts to address hunger and malnutrition.
- Successfully raised over $4,000 for the distribution of 1,100+ face shields to frontline workers during the critical COVID-19 pandemic, enhancing their safety and protection.
- Orchestrated a 4-member team to craft captivating digital content for social media, significantly boosting user engagement and establishing our team as digital marketing innovators.
- Drove innovative business strategies at VIT Chapter, fueling substantial sales growth and positioning us as industry leaders.
- Organized and led multiple impactful blood donation campaigns, garnering more than 1,500 voluntary donors. Spearheaded public awareness initiatives to educate the community about the detrimental effects of alcohol and drug abuse, collaborating with local organizations for broader impact.
- Demonstrated strong leadership by guiding a dedicated team, fostering community engagement, and inspiring fellow youth to become active agents of positive change.
Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), SQL, JavaScript, Flask (Streamlit), R-Programming
Power BI, Tableau, Excel, Regression Models, Feature Engineering, Time Series Analysis, Hypothesis Testing
PyTorch, TensorFlow, HuggingFace, Transformers, Computer Vision, Neural Networks, Decision Trees
MySQL, SQLite, Oracle, Salesforce, Firebase, AWS, Azure, Snowflake, Apache Spark, Hadoop, Airflow, ArcGIS
August 2022 - April 2023
Abhinav Gupta, Somil Kuchhal, Dr Jayashree J
In today’s research world, sentiment analysis has been tested over several different machine learning models but with our study, we combined multiple different models and then used a Voting Classifier to determine the best. To increase the diversity and accuracy of our models, we have created an umbrella dataset constituting three popular datasets. We further created a more focused set of 75,000 entries from this larger dataset by developing a randomization algorithm. The supervised learning algorithms we have designed and tested are Random Forest, Extra Tree Classifier, Decision Tree, Logistic Regression, and XG Boosting. Finally, we concluded that Extra Tree Classifier has the best accuracy and precision.
July 2022 - September 2022
Raunak Shukla, Panshul Jindal, Abhinav Gupta, Dr Hemprasad Y Patil
Grook is a sports management app that provides a solution for people to book sports facilities in their neighborhood for particular time slots to ensure a free space to play. It also allows the owners to manage their facilities more efficiently and also aggregate all available sports facilities in the vicinity of the user's location. We have built and trained our model to predict the total revenue generated on a facility using ensemble Machine Learning Regression algorithms. In addition to the existing features in the dataset like sports, days, months, etc., we have also added our polynomial features. We’ve tested algorithms including Simple, Polynomial, Ridge, Lasso, Elastic Net Regression Techniques, and Decision Tree, Support Vector, and Stochastic Gradient Descent Regressors making special models for each technique to get the best results. The polynomial features have been tested over several degrees to get an even better understanding of our model’s accuracy.
October 2022 - December 2022
Abhinav Gupta, Raunak Shukla, Panshul Jindal, Dr Hemprasad Y Patil
We have utilized a unique dataset of 50,000 review inputs in this research to perform sentiment analysis using a selection of machine learning classification methods. In order to get the best results, we evaluated numerous classification techniques, including Logistic Regression, Random Forest, Extra Tree Classifier, Support Vector Machine (SVM), and Naïve Bayes. We also designed a Feed Forward Neural Network Engine that works like a classification tool and outputs a sentiment in yes(1) or no(0). The data in the Neural Network is routed via multiple layers, where it is refined to get the best outcomes.
Science
Major: Computer Science
GPA - 3.75 / 4.0
Relevant Coursework -
- Machine Learning (CSCI 567)
- Deep Learning and Its Applications (CSCI 566)
- Applied Natural Language Processing (CSCI 544)
- Analysis of Algorithms (CSCI 570)
- Multimodal Probabilistic Learning of Human Communication (CSCI 535)
- Database Systems (CSCI 585)
- Information Retrieval (CSCI 572)
Major: Computer Science
GPA - 9.05 / 10.0
Relevant Coursework -
- Artificial Intelligence (CSE 3013)
- Data Structures And Algorithms (CSE 2003)
- Human Computer Interaction (CSE 4015)
- Advanced Aptitude And Reasoning Skills (STS 2202)
- Blockchain And Cryptocurrency Technologies (CSE 1006)
- Statistics For Engineers (MAT 2001)
- Calculus For Engineers (MAT 1011)
- Applied Linear Algebra (MAT 3004)
- Social And Information Networks (CSE 3021)