Aspiring CS Engineer - Full-Stack & Data Analytics

Yogita Pardeshi

+91-820******* — ************@*****.***

GitHub — LinkedIn

EDUCATION

Master of science in Computer Science 2024 – Present Department of Computer Science, Savitribai Phule Pune University Bachelor of Science in Computer Science 2021 – 2024 ARB Garud College Shendurni, Kaviyatri Bahinabai Chaudhari North Maharashtra University CGPA: 8.24

SKILLS

Languages: C, C++, Python, JavaScript

Web: HTML, CSS

Database: PostgreSQL, Neo4j

Concepts: OOP, Data Structures and Algorithms

Tools: Git, VS Code, Linux, Jupyter Notebook

Libraries: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn PROJECTS

Custom Slideshow Web Application

• Developed a full-stack web application to create, edit, and manage interactive slideshows

• Implemented drag-and-drop functionality for dynamic slide arrangement and layout customization

• Enabled saving and loading slideshows using user-defined names for intuitive access and management

• Optimized data handling for faster retrieval and smooth user experience

• Designed a relational database schema to store slideshow data, layouts, and user inputs efficiently

• Built a responsive and user-friendly interface with real-time editing features

• Technologies: HTML, CSS, JavaScript, PostgreSQL

Graph-Based Recommendation System

• Developed a knowledge graph-based recommendation system to model complex relationships between users, products, and interactions.

• Designed and implemented a graph database using Neo4j and Cypher for efficient data storage and querying.

• Built a collaborative filtering model using the ALS algorithm to generate personalized recommenda- tions.

• Integrated Python tools (Pandas, Py2neo, Scikit-learn) for data preprocessing, graph interaction, and model development.

• Evaluated system performance using RMSE, Precision, Recall, and F1-score.

• Technologies: Python, Neo4j, Cypher, Py2neo, Pandas, Scikit-learn Supply Chain Optimization System

• Developed a system for demand forecasting, inventory optimization, and vehicle routing problems

• Implemented linear regression for demand prediction

• Applied linear programming considering constraints such as capacity and cost

• Solved vehicle routing problem for optimized delivery paths

• Visualized results using Matplotlib

• Technologies: Python, NumPy, pandas, Scikit-learn, SciPy, Matplotlib STRENGTHS

• Ability to work independently as well as collaboratively in team environments.

• Quick learner with strong adaptability to new technologies and problem-solving approaches.

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