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Health and Wellness Management App (In Progress)

Development Timeline: Present - December

Team: 5 members

Role: Technical Lead

As the team leader, I proposed and am overseeing the development of the Health and Wellness Management App. This application addresses the challenge of managing both physical and mental health by integrating various tracking and management functionalities into a unified platform. The app will provide users with the ability to track their workouts, monitor their mood, and manage their medications and vitamins, all while offering personalized analytics that connect different aspects of health.

The app is designed to solve the problem of fragmented health tracking tools by providing a holistic solution that consolidates physical fitness, mental well-being, and medication management. By using machine learning algorithms in Python, the app will offer insights into how different health factors interact, such as the impact of regular physical activity on mental health. For example, the app might highlight trends like "improved mood after physical activity," providing users with a clearer understanding of their overall health.

Key features include daily reminders for medications and vitamins, activity and mood tracking, and personalized health insights through data visualizations. The project will use React Native for front-end development, MongoDB for efficient data storage, and Python-based analytics to generate meaningful health trends. This approach enables users to visualize their progress and identify patterns, helping them make informed decisions about their health.

With a team of five members, the app is scheduled to remain in development until mid-December, at which point it will be a comprehensive tool for health-conscious individuals seeking a unified platform for managing both physical and mental health.

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Movie Attendance Predictive Analytics

Development Timeline: January 2024 - April 2024

Team: 8 members

In this project, we conducted predictive analysis to determine the likelihood of a person going to the movies based on factors like age, preferred genre, and preferred movie length. The target variable was whether a person would attend a movie, as movie theater profit depends heavily on movie attendance.

We employed multiple machine learning models, including Decision Trees, Artificial Neural Networks (ANNs), and Logistic Regression, to predict movie attendance. Each model was evaluated using performance metrics such as accuracy, precision, recall, and F1-score.

The Decision Tree model identified key factors like age and movie intensity to predict movie attendance, achieving high accuracy in certain nodes. The ANN model, constructed using TensorFlow’s Keras API, provided insights into the relationships between demographic factors and movie attendance over multiple epochs. Logistic Regression offered interpretable results in terms of likelihood and performed well for this binary classification task.

Through data visualization techniques like confusion matrices, ROC curves, and heatmaps, we observed strong predictive performance across all models. These findings highlighted that younger audiences prefer action and thriller movies, while older audiences are more likely to attend longer movies, particularly dramas or historical genres.

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Encoder and Decoder

This project involves the development of a Java-based message encoder and decoder using Substitution Cipher and Shuffle Cipher techniques. The goal was to provide users with the ability to encode and decode messages by selecting a cipher type, inputting a message from a file, and specifying whether the message should be encoded or decoded.

The Substitution Cipher works by shifting each letter in the message by a specified amount, while the Shuffle Cipher scrambles or unscrambles the message a specified number of times. The user can choose between these two methods to process their messages. The system also allows the user to specify input and output file names for reading and saving the encoded or decoded messages.

The project was developed in Java using IntelliJ IDEA, and it incorporates file I/O to handle reading and writing messages to and from text files. Key features include an error handling system, the ability to process both encoding and decoding requests, and a user-friendly way of selecting cipher options. The project's algorithms are optimized for quick and efficient encoding and decoding, ensuring that even large files can be processed.

The main program architecture includes:

• SubstitutionCipher: Handles encoding and decoding by shifting characters in the message.
• ShuffleCipher: Provides the functionality to shuffle or unshuffle the message multiple times.
• File I/O: Facilitates reading input messages from files and writing the encoded/decoded messages to output files.

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Data Structures, Games, and Algorithm Optimization

These projects focused on developing C++ programs that implemented various data structures, interactive games, custom exception handling, and performance testing of sorting algorithms. Through hands-on experience, I gained foundational computer science knowledge and applied optimization techniques to improve program efficiency.

• Data Structures: Implemented multiple data structures such as arrays, linked lists, stacks, queues, binary trees, and hash tables to perform operations like insertion, deletion, and traversal efficiently.
• Interactive Games: Developed games like Tic Tac Toe and Bingo with C++, managing game logic and states using appropriate data structures to handle user interactions and decisions.
• Exception Handling: Created custom exceptions to handle invalid inputs and file operations, ensuring robustness through try-catch blocks and enhancing program stability.
• File I/O: Enabled saving and loading of game states and sorting inputs, allowing persistent data storage and retrieval for testing and gameplay.
• Algorithm Optimization: Tested the performance of sorting algorithms (Bubble Sort, Insertion Sort, Merge Sort, Quick Sort, and C++'s built-in sort) under different input scenarios, using the `chrono` library to measure execution time and identify the most efficient approach.

These projects ultimately showcased the importance of choosing the right data structures and algorithms based on the problem's complexity. The algorithm performance tests highlighted the differences between simplicity and speed, with complex algorithms like Merge Sort and Quick Sort outperforming simpler methods for large datasets.

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Hospital Billing System

The Hospital Billing System project was designed to simplify hospital billing processes by allowing input of patient and doctor information, calculating charges, and generating detailed bills. The system captures key data such as patient ID, name, date of birth, admission and discharge dates, attending physician details, and charges associated with services like room, pharmacy, and doctor fees.

The project is built using object-oriented principles in Java, with the following classes playing a central role:

• Person: A base class that represents individuals with attributes like first name and last name.
• Doctor: A subclass of Person, which adds the doctor’s specialty as an attribute.
• Patient: Another subclass of Person, this class captures additional patient-specific information like ID, date of birth, admission/discharge dates, and the assigned physician.
• Date: Handles date-related functionalities, such as setting and printing admission and discharge dates.
• Bill: Manages billing details, such as charges for pharmacy, room, and doctor services, and calculates the total bill for each patient.

The system enables the entry of multiple patients and doctors, calculates the total charges for each patient, and generates a bill. Both the patient information and the generated bill are printed to the console and saved to a text file with the patient’s name for easy reference.

The application is user-friendly and provides a loop for continuous input of patient and doctor details until the user decides to stop. The bill generation process is automated, ensuring accuracy in calculations and formatting. By providing both console output and file storage, the system ensures that all data is available for future reference.