Firearm-related fatalities have been a pressing issue in the United States, prompting discussions and debates on gun laws and public safety. To shed light on this important topic, this article delves into an extensive analysis of firearm death data spanning from 1981 to 2021 across all states in the US.
This Python script utilizes various natural language processing and machine learning tools to create a conversational system for answering questions related to uploaded PDF documents. The system incorporates tools from the Langchain library to facilitate text processing, embeddings, and conversational chains.
The analysis presents a comprehensive analysis of crime distribution and trends in the city of Seattle for the years 2008 to 2022, emphasizing year 2022 to understand the present crime dynamics in Seattle, by utilizing a robust dataset obtained from the Seattle Police Department. The dataset is a rich amalgamation of information, detailing the types of offenses, the locations where they occurred, the times at which they took place, and other critical variables that give a multi-dimensional view of criminal activity within the city.
Alcohol consumption among youth is a significant public health concern with potentially harmful consequences for both individuals and society. In this study, we aim to predict alcohol consumption among youth using data from the National Survey on Drug Use and Health (NSDUH). The NSDUH is an annual survey that collects data on substance use and related behaviors among individuals aged 12 years and older in the United States. Our attention is directed towards various factors such as religious convictions, the dynamic between parents and their children, academic performance, and engagement in groups aimed at improving self-esteem and problem-solving abilities. By analyzing these factors, we hope to identify potential risk and protective factors that could inform prevention and intervention efforts to reduce alcohol consumption among youth.
Seattle implemented a Vision Zero initiative in 2015 with the goal of eliminating traffic deaths and serious injuries on city streets by 2030. However, in the past two years, deaths and serious injuries from traffic collisions have been on the rise, indicating that current initiatives may not be enough. The project aims to use Seattle’s collision dataset to analyze different factors that could inform street redesigns or alternative modes of transit programs.
The core objective of this project is to develop an image caption generator. This software will prove invaluable for individuals with visual impairments, empowering them to generate accurate and descriptive captions for images they cannot see. At the heart of the mission is the commitment to inclusivity and accessibility. It’s important to recognize the challenges faced by individuals with visual impairments in understanding and interacting with the visual world. The software aims to bridge this gap, providing a means for the visually impaired to access and comprehend visual content with the aid of descriptive captions.
The issue of building energy consumption and its environmental impact in Seattle is a pressing concern. This research, based on Seattle’s Building Energy Benchmarking Program, aims to address the problem by developing a sophisticated model that can accurately analyze and forecast energy consumption patterns in various city buildings in Seattle. The study utilized the combination of supervised and unsupervised learning methods such as linear regression, clustering, and dimensionality reduction to discover underlying trends, patterns, and insights hidden in the large data set. By gaining a deep understanding of the dynamics of energy use, the research actively aims to promote sustainable practices, improve informed decision-making, and effectively reduce the environmental impact of building energy use in Seattle in real-time.
In this study, our primary objective was to develop a predictive model to determine whether a dwelling is occupied by its owner or rented out, using a range of significant factors including income, education, and marital status. By accurately classifying the dataset, we aimed to utilize the Support Vector Machine technique, employing both the Radial Basis Function (RBF) and polynomial kernel.
The objective of this research is to classify the bird species based on their chirping sounds, which were recorded in mp3 format. The study utilizes data from the Birdcall competition dataset, sourced from the Xeno-Canto bird sound archive. I have worked on modeling a binary and multi-class classification problem to identify birds based on their chirping sounds. Neural Networks are being used in this case with different activation functions such as Relu, tanh, SoftMax, or Leaky Relu. I have also explored the effectiveness of using a CNN model and transfer learning from other paradigms to accurately classify the sounds of birds.
This project seeks to address the inefficiency of local libraries in Seattle by developing a scalable and efficient library management system. The new system will empower staff to manage essential library functionalities, improve patron experience, and foster a culture of lifelong learning and discovery. This will ultimately result in better library services and a positive impact on the community.
The analysis will attempt to identify the core cause of departure delays for United Airlines aircraft departing from New York City in 2013. The goal is to decrease flight delay time by determining what variables are causing the delays.This will enable United Airlines customers to improve those aspects, hence increasing customer happiness and flying effectively. The study focuses on environmental characteristics such as the time of day, year, temperature, wind speed, precipitation and visibility. Our analysis will be carried out by leveraging the nycflights13 dataset. This dataset contains the departure timings for all flights departing from New York City’s three airports - La Guardia (LGA), John F. Kennedy (JFK), and Newark Liberty International Airport (EWR) in 2013. For the scope of this project, we will be focusing on the data related to United Airlines.
This project aims to analyze the trend in tweets related to the recession from October 2022 to December 2022, with a focus on identifying instances of layoffs. The study will use data from tweets during this time period to understand the current state of the recession and any potential impacts on employment.
list of over 10,000 fast food restaurants provided by Datafiniti’s Business Database
This project aims to analyze the trend in tweets related to the recession from October 2022 to December 2022, with a focus on identifying instances of layoffs. The study will use data from tweets during this time period to understand the current state of the recession and any potential impacts on employment.
The analysis is conducted on the greatest 500 songs of all time. The dataset is taken from Kaggle.
I am currently pursuing my master’s degree at Seattle University, I often find it challenging to navigate the campus and locate essential amenities such as cafes and bicycle parking. To address this issue, I decided to make a comprehensive map of Seattle University. This map is designed to assist students, faculty, and visitors in effortlessly navigating the campus, providing a quick and efficient way to locate key areas. The primary focus of this project is to facilitate easy identification of cafes and bicycle parking spaces within the university, enhancing the overall experience for those exploring the campus.