Holy Angel University
Change the repository type filter
All
Repositories list
38 repositories
.github
PublicStudent-focused organization where the next generation of engineers are nurtured.- It serves as a collaborative space where students and instructors can upload and share their activities, assignments, and projects related to DSA. The repository is designed to foster learning, collaboration, and knowledge sharing among students and instructors.
- A comprehensive management system built using Django, a high-level Python web framework. The system includes functionalities for user authentication, profile management, product management, and order processing.
job-listing-platform
PublicAa comprehensive web application designed to connect employers with potential employees. This platform allows employers to post job listings, manage applications, and communicate with candidates.task-management
PublicA robust application designed to streamline task management and collaboration. This application empowers project leaders to effectively oversee tasks, while project workers can actively contribute and track their progress.- A comprehensive web application designed to streamline the process of booking and managing doctor appointments.
- This is a simple yet powerful todo list application designed to help users manage their tasks efficiently.
- It personalizes your itinerary based on preferences and seamlessly integrates booking through the same platform. Explore new destinations, create the perfect trip, and book everything in one place!
ai-quiz-generator
PublicThis project is a quiz generator that uses a dataset of questions and answers to generate a quiz.liquor-brands-analysis
PublicThe project revealed interesting insights into liquor brands, particularly focusing on the distribution and relationship between registration numbers and the number of wholesalers.- Predict USA/EUR foreign exchange rate depending on the country and based on history data.
- The project aims to analyze and predict life expectancy at birth, which reflects the average number of years a newborn infant would live if current mortality trends persist throughout their lifetime.
- This project analyzes MIGA's income statement data (2012-2024) to understand income sources, expense trends, and predict future performance. Income seems stable, while expenses vary. Time-series forecasting models (Prophet, Random Forest) suggest potential future trends, aiding MIGA in strategic financial planning and resource allocation.
diabetes-prediction
PublicThe project entails the factors of diabetes and health markers which help undestand whether there is a correlation of predicting of getting diabetes and its accuracy.- This project aims to develop a predictive model to forecast border crossing volumes using historical data on U.S. land port entries. With increasing cross-border movement, the ability to accurately predict border traffic is essential for efficient resource allocation, traffic management, and strategic planning at ports of entry.
- This project aims to analyze global trends in pharmaceutical drug spending, focusing on variations over time and across different countries. By investigating these trends, we can gain insights into the economic impact of drug expenditures and assess the factors driving pharmaceutical costs.
- By analyzing historical gold price data and identifying trends, the project seeks to provide accurate forecasts that can aid investors and analysts in making informed decisions in the commodities market. The use of different regression methods allows for a comprehensive comparison of performance and accuracy in price prediction.
- Builds a model to predict demand class (Low, Medium, High) for warehouse and retail sales. It analyzed historical data to identify sales trends and used various machine learning models.
- This project aims to analyze historical Brent oil prices and predict future prices using various machine learning algorithms. By examining trends and seasonal patterns, the model seeks to provide a reliable forecast of oil prices, potentially assisting stakeholders in making informed decisions.
- This project analyzes the City Population Annual Timeseries dataset from the UN Statistics Division to understand population trends across cities from 1972 to 2014. The goal is to evaluate population changes over time, investigate population distributions by city type and sex, and develop predictive models for future population estimates.
- This project analyzes global population trends using World Bank data. Findings show population growth varies by region, with faster growth in Sub-Saharan Africa and South Asia. Machine learning models predict future trends, with Logistic Regression excelling in identifying population growth.
- This project analyzes a tobacco use survey to understand trends. We identified shifting patterns across age, education, and geography. Machine learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting) achieved 100% accuracy, aiding public health efforts to reduce tobacco consumption.
- Analyzes CO2 data (1958-2024) from Mauna Loa, Hawaii, revealing a steady increase. Visualizations show upward trends and rising CO2 levels by decade. Machine learning models predict future levels, with Random Forest performing best. Findings highlight urgency for action to curb CO2 emissions and protect our environment.
- This project analyzes historical glacier mass balance data to understand the impact of climate change. Findings show a decline in mass balance, with Decision Tree and Random Forest models most accurate in predicting future trends. This emphasizes the urgency for addressing climate change to protect glaciers and vital water resources.
- This project analyzes broadband availability in US counties (October 2020). We explore distribution patterns and use machine learning to classify availability (high, medium, low) based on FCC data.
- This project explores house price trends across US cities using a historical dataset. Visualizations reveal city-specific price fluctuations and overall market movements. Random Forest and K-Nearest Neighbors models achieved high accuracy in predicting future prices based on historical trends, suggesting their potential for market analysis.
air-quality-of-nyc
PublicThis project analyzes NYC air quality data to identify common pollutants and explore spatial/temporal trends. It utilizes various regression models, with Gradient Boosting Regressor achieving the highest accuracy (95.58%) in predicting air quality values. Findings can inform strategies to improve public health and reduce air pollution.- This project analyzes US health data to understand how diet, physical activity, and demographics impact obesity rates. Findings show correlations between lower activity, poor diet, and higher obesity.
- Analyze fitness dataset to uncover insights into exercise patterns, calorie consumption, body composition, and overall fitness levels. Explore correlations between workout type, frequency, and physiological markers. Use data analysis techniques to provide a comprehensive understanding of fitness behaviors and factors influencing exercise outcomes.
databank
PublicCurated collection of publicly available datasets for student experimentation & data analysis projects for educational purposes. Serves as a central hub for diverse datasets collected from different online sources. Each dataset includes a brief description of its content. Intended for educational use, research, analysis & more.