Gurjaspreet Singh
@gurjaspreet27
Gurjaspreet Singh
@gurjaspreet27
I am a Computer Science undergraduate at Chandigarh University, am proficient in multiple programming languages, and have hands-on experience with data analysis and web development.
I am a Computer Science undergraduate at Chandigarh University, am proficient in multiple programming languages, and have hands-on experience with data analysis and web development.
Mohali, India
Gurjaspreet Singh
Aspiring Software Engineer
Contact me: Email | 8728998900
Location: SAS Nagar, India
Connect with me: GitHub | LinkedIn | LeetCode | HackerRank
Education
- Chandigarh University - BE Computer Science & Engineering (2021 - Present)
- CGPA: 8.46
- National Public School - 12th Standard - CBSE Board (2019 - 2021)
- Percentage: 96.6%
Training and Internship
- Intel | Intel Digital Readiness Program - Chandigarh University (June 2023 - July 2023)
- Proficiently utilized Python and key libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, OpenCV) for in-depth data analysis, visualization, and machine learning. Applied tools like Tableau and Orange Data Mining to extract valuable insights from complex datasets.
- Developed a Linear Regression-based Predictive Maintenance Model, enhancing equipment maintenance efficiency and achieving cost savings in a targeted context.
- Implemented a Movie Recommender System using the k-Nearest Neighbors (kNN) algorithm, improving user engagement with personalized movie recommendations.
Skills
- Programming Languages: C++, Java, Python, R, JavaScript, HTML, CSS
- Libraries/Frameworks: NumPy, Pandas, Matplotlib, Scikit-learn
- Tools / Platforms: GitHub, Tableau, VS Code, Jupyter Notebook, RStudio
- Databases: MySQL
Projects / Open-Source
- FlyTEX: Flight Price Prediction - Python, HTML, CSS, Flask
- Developed a Machine Learning-powered Flight Price website, predicting costs for informed decision-making.
- Implemented a user-friendly GUI, ensuring swift and precise outcomes for enhanced user experience.
- Super Mart Grocery Sales Analysis using Tidyverse - R, RStudio, Kaggle
- Analyzed supermarket sales using R and Tidyverse, focusing on data cleaning, regional and categorical analysis, and top customer identification for a dataset covering customer orders in Tamil Nadu, India.
- Cardiovascular Disease Prediction - Orange, Seaborn
- Employed various machine learning algorithms, including k-nearest neighbors, Decision Trees, Random Forest, and Neural Networks, to analyze a comprehensive cardiovascular disease risk prediction dataset.
- Attained an outstanding model accuracy of 91.7% using a Neural Network-based approach.