Welcome to the Technology and Data Hub! Here, you'll find an extensive collection of projects spanning various domains, including web design, data mining, SQL, machine learning, and more. These projects cover a diverse range of topics, providing a rich resource for exploration and learning in the field of technology and data.
My repository is meticulously curated to showcase a diverse array of projects, each meticulously designed to spotlight a multitude of techniques, algorithms, and applications in various fields including machine learning, data science, web designing, SQL, and agile methodology. Whether you're a novice seeking to build a foundational understanding or a seasoned practitioner in pursuit of advanced insights, these projects provide invaluable opportunities to delve into a myriad of facets within data analysis, modeling, and prediction.
We have meticulously documented each project, providing comprehensive insights into the methodologies employed and the results obtained. You will find detailed explanations, code samples, and visualizations to aid your understanding.
- K-Mean Clustering
I adeptly implemented the K-means clustering algorithm, a potent tool for unsupervised learning. This algorithm, recognized for its efficiency, was applied to group data points into distinct clusters, unraveling concealed patterns within the datasets.
- Classification and Regression Trees
In our loan approval prediction project, we employed the CART algorithm to build a decision tree using features such as Debt to Income Ratio, FICO score, and Request Amount. Guided by the Gini index, we systematically reduced features at each node, optimizing splits for enhanced model simplicity. This approach facilitated the creation of a streamlined decision tree that not only predicted loan approval but also provided a clear understanding of the most influential features in the decision-making process.
- CheapFare Airline Reservation Relational Database
Our group was tasked with designing and implementing a relational database for CheapFare, an airline reservation company specializing in domestic and international flight bookings. The goal was to create a robust database system to manage bookings, flights, e-tickets, travelers, and other relevant entities efficiently.Designing and implementing a relational database for CheapFare, an airline reservation company specializing in domestic and international flight bookings. The goal was to create a robust database system to manage bookings, flights, e-tickets, travelers, and other relevant entities efficiently.
- Web Design and Rental Application Development
The server-side application developed using Node, Express, and Handlebars enables seamless item rentals by providing a user-friendly interface and efficient functionality. Through intuitive Handlebars templates for home and error pages, the application enhances user experience and accessibility, ensuring a smooth browsing and interaction process. With a robust data model for rental items, including unique IDs and properties, data management processes are streamlined, facilitating effective organization and retrieval of information. Additionally, various functionalities such as search, rental, return, and result updates are seamlessly integrated through dedicated server endpoints, ensuring smooth and efficient operation of the application.
- Analog Circuit Simulator
This project aims to develop Analog Circuit Simulator (ANASIM), capable of simulating analog electronic circuits using numerical methods, machine learning, and heuristic algorithms. ANASIM is designed to address the challenge of simulating nonlinear behavior in circuits through various approaches. Theoretical background encompasses numerical methods like Euler's and Runge-Kutta methods, machine learning cost functions for approximating real data, and heuristic algorithms for faster problem-solving. Work breakdown includes implementing a cost function for predicting component currents, integrating circuit components such as resistors, capacitors, and inductors, and utilizing OpenGL for visualization. A sample run involves applying sinusoidal voltage and plotting results to display applied voltage and component voltages over time.
- Web Design and Yoga Application Development
This web application streamlines the process of booking yoga classes, offering users a seamless platform to create accounts, securely log in, view available classes, and book preferred sessions. With an intuitive user interface, users can easily navigate through class details, including schedules and instructor information, and complete bookings with payment information provided. Additionally, an admin section enables efficient management and monitoring of user payments, displaying key details such as usernames, class names, and payment records. Powered by MongoDB for database storage and Handlebars templating for dynamic HTML rendering, the application ensures a consistent and responsive user experience across devices. Integrated with Express.js and express-session for robust session management, the system distinguishes between regular users and administrators, providing enhanced security and functionality.
- Car Price Prediction Model
This repository contains the implementation of a reliable predictive model that can be used to estimate car prices based on various features. The project approaches this as a regression task, where the target variable is the selling price of the cars. To evaluate and discover patterns from the dataset, which includes details on the qualities of automobiles, the project utilizes a variety of machine learning algorithms. The implementation of this project is constrained by memory and time utilization considerations, and memory and time profiling were employed to assess the models' effectiveness in terms of computational needs and prediction accuracy. These constraints help in creating a balance between efficiency and accuracy, resulting in a solution that is computationally feasible while delivering precise and reliable predictions. The design decisions are also influenced by broader societal, economic, and ethical aspects, in addition to the technical aspects. The dataset used in this project can be obtained from Kaggle at [https://www.kaggle.com/datasets/nehalbirla/vehicle-dataset-from-cardekho?select=Car+details+v3.csv], and it consists of 8,128 samples with several features such as year, mileage, engine specs, maximum power, seats, and more, providing a comprehensive examination and analysis of the factors impacting vehicle pricing in the market.