Manjit Baishya's Projects
This project contains SQL scripts to create and manage a database for an amusement park. The database is designed to store information about rides, visitors, tickets, staff, facilities, maintenance records, ride reviews, ride assignments, ride entries, and visitor feedback.
This project focuses on predicting breast cancer using machine learning models through cross-validation techniques. The dataset utilized in this study is the Breast Cancer Wisconsin (Diagnostic) dataset.
This Parcel Management System is designed to efficiently manage parcels, customers, sellers, hubs, routes, delivery persons, staff, managers, and buyers within a delivery network. It provides a comprehensive solution for tracking parcels from order placement to delivery.
Here is a new management system designed to help you manage your bookstore efficiently. Keep track of books, authors, customers, orders, publishers, genres, and more with ease!
The database schema includes tables to store information about vehicles, owners, traffic incidents, traffic tickets, traffic officers, payments, ticket histories, and violations. Relationships are established between entities to maintain data integrity.
This project analyzes laptop price data from Flipkart using AutoScraper for web scraping. It includes data loading, EDA, cleaning, statistical analysis, and visualization. The goal is to derive insights for pricing strategies and market positioning. Explore the repository for detailed documentation and code.
This Accommodation Booking System is designed to facilitate the booking and management of listings, guests, hosts, bookings, amenities, and reviews.
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This project aims to predict the obesity levels of individuals based on various features such as age, height, weight, and lifestyle factors. Multiple machine learning models are utilized, including Logistic Regression, Support Vector Machine (SVM), and ensemble methods like Bagging with SVM.
This project aims to predict taxi fare amounts in New York City using a dataset of historical taxi rides. We employ machine learning techniques to create models that can estimate the total fare amount based on various features of the trips.
This project analyzes hate crimes reported in the NYPD using data from a Kaggle dataset. It explores temporal and spatial patterns, offense types, bias motivations, and arrest trends. The analysis involves data cleaning, preprocessing, and visualization to reveal insights into crime trends and geographical concentrations.
This Arduino project implements a PAC-MAN-style game using an LCD display and push buttons. Players control a snake to eat randomly placed "monsters" for points. Hardware includes an Arduino board, LCD with I2C, and push buttons. Code manages game logic, button inputs, snake movement, and score display on the LCD.
This project has effectively analyzed house price trends and prediction using machine learning, emphasizing data cleaning, exploratory analysis, and regression modeling to gain insights into dataset patterns and structures.
his project focuses on predicting house prices in various cities of Pakistan using machine learning techniques. The dataset contains features such as property type, location, city, number of baths, purpose (e.g., for sale), number of bedrooms, area in square feet, and price.
The "Raisin Variety Prediction" project involves data analysis, cleaning outliers, preprocessing data, and training machine learning models for predicting raisin varieties. The best-performing model is chosen based on accuracy scores for deployment.
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