Accident Detection Model using Deep Learning, OpenCV, Machine Learning, Artificial Intelligence.

FFmpeg 7.1 for Google Colab

Real-time active shooter detection system using YOLOv11 for gun and pose recognition. Still under development and training. Designed for research and early-stage security applications.

The project objective is to generate automated commentary for the cricket videos using computer vision and neural networks

Implementation of the fast neural style transfer algorithm on Keras. Includes Jupyter notebooks, python script and web app.

YBI Foundation Internship : Hands-on Project and Capstone Project

🔁 Copy public Google Drive & MEGA.nz files directly to Google Drive using Google App Scripts & Collab— no login required, perfect for large file transfers and cloud backups.

Free ComfyUI notebook for Kaggle with GPU support

The aim of this project is to develop a robust sentiment analysis system that can automatically classify restaurant reviews as positive, negative, or neutral based on the sentiment expressed in the text.

Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. So, in this application, we are asking a YouTuber to enter the channel id and a particular timeline. By using the channel id and timeline we are performing sentiment analysis on his videos by fetching the subtitles of their videos in a particular timeline given by the YouTuber.Basically performing intent and emotion classification on his video subtitles.

models built using various machine learning techniques to predict floods and earthquakes

Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. Simulated annealing is a probabilistic technique for approximating the global optimum of a given function.

A groundbreaking initiative aimed at enhancing the independence and quality of life for individuals suffering from blindness and visual impairment. Navigating the world with limited vision presents numerous challenges, and our project addresses these difficulties through the integration of artificial intelligence and computer vision technologies.

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Performed ETL processes in the cloud to upload a dataframe to an RDS instance and used PySpark to perform a statistical analysis on Amazon datasets.

DiversiFAI – AI That Sees Beyond Gender, Empowering Equality.

This project showcased the ETL process of big data. Raw data about Amazon video games reviews was collected from a site, placed into an AWS database, and queried against using Pyspark and SQL to find out whether Amazon vine reviews influenced customer feedback.

Repositorio con análisis de los data ENAHO de los módulos...

Machine Learning Models

Michigan State University Data Analytics Neural Network Challenge

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4° Projeto mensal do semestre 2024.2 - 6° Período

Analyze and predict if a flight will depart on time using airport data.

This is my graduate thesis, a mobile applicaiton with computer vision

AI-Based Mental Health

Find songs, animes and podcasts based on your mood

Potato disease classification model

Competition Description MNIST ("Modified National Institute of Standards and Technology") is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike. In this competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images. We’ve curated a set of tutorial-style kernels which cover everything from regression to neural networks. We encourage you to experiment with different algorithms to learn first-hand what works well and how techniques compare. Practice Skills Computer vision fundamentals including simple neural networks Classification methods such as SVM and K-nearest neighbors