Weather Application
Flutter, Dart, Firebase, OpenWeather API, WAQI API
• Built with Flutter (Dart) for cross-platform
• Built with Flutter (Dart) for cross-platform mobile development
• Uses Firebase for authentication
• Fetches weather data from OpenWeatherMap and air quality from WAQI APIs
• Detects user location or allows city search for weather info
• Displays current weather, 5-day forecast, AQI, humidity, pressure, wind, clouds, sunrise/sunset
• Features animated, modern Material Design UI with dynamic weather visuals and info cards
• Built with Flutter (Dart) for cross-platform mobile development
• Uses Firebase for authentication
• Fetches weather data from OpenWeatherMap and air quality from WAQI APIs
• Detects user location or allows city search for weather info
• Displays current weather, 5-day forecast, AQI, humidity, pressure, wind, clouds, sunrise/sunset
• Features animated, modern Material Design UI with dynamic weather visuals and info cards
Image 1
Image 2
Image 3
MotorNation Website
HTML, CSS, Javascript, Express.js, Node.js, PostgreSQL
• Uses HTML, CSS, and JavaScript for development
• Serves as the website for MotorNation, focused on automotive themes
• Provides navigation menus, multiple content sections, and interactive elements
• Showcases vehicles, services, or automotive content with images and descriptions
• Features responsive design for mobile and desktop viewing
• Uses CSS for custom styling, layouts, and visual effects
• Includes contact or feedback forms for user interaction
• Implements clear structure and user-friendly navigation throughout the site
• Serves as the website for MotorNation, focused on automotive themes
• Provides navigation menus, multiple content sections, and interactive elements
• Showcases vehicles, services, or automotive content with images and descriptions
• Features responsive design for mobile and desktop viewing
• Uses CSS for custom styling, layouts, and visual effects
• Includes contact or feedback forms for user interaction
• Implements clear structure and user-friendly navigation throughout the site
DailyHelper
React-Native
• Built entirely with JavaScript
• All-in-one app aimed at managing various aspects of college life
• Includes features such as scheduling, note-taking, task management, and organization tools
• User interface is simple and functional, focused on accessibility and ease of use
• Design prioritizes practical daily management and quick access to key functions for students
• All-in-one app aimed at managing various aspects of college life
• Includes features such as scheduling, note-taking, task management, and organization tools
• User interface is simple and functional, focused on accessibility and ease of use
• Design prioritizes practical daily management and quick access to key functions for students
College Companion App
Flutter, Dart, Google Maps API, GPS Integration
• Uses Dart (Flutter) as the primary tech stack
• Functions as a mobile app to assist with various aspects of college life
• Offers features such as scheduling, reminders, notes, and organization tools
• Provides a modern, responsive UI following Material Design principles
• Focuses on ease of use and accessibility for students
• Functions as a mobile app to assist with various aspects of college life
• Offers features such as scheduling, reminders, notes, and organization tools
• Provides a modern, responsive UI following Material Design principles
• Focuses on ease of use and accessibility for students
Flight Delay Prediction
Python, TensorFlow, Scikit-Learn, Pandas, Matplotlib, Seaborn
• Uses Python with libraries such as pandas, scikit-learn, TensorFlow, seaborn, matplotlib, and
joblib
• Reads and preprocesses flight data, encoding categorical variables and scaling features
• Visualizes feature correlations using heatmaps
• Implements a Random Forest classifier and a neural network (with Keras/TensorFlow) to predict flight delays
• Splits data for training and testing, evaluates accuracy, and saves trained models for reuse
• Reads and preprocesses flight data, encoding categorical variables and scaling features
• Visualizes feature correlations using heatmaps
• Implements a Random Forest classifier and a neural network (with Keras/TensorFlow) to predict flight delays
• Splits data for training and testing, evaluates accuracy, and saves trained models for reuse
