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

FFmpeg 6.0 for Google Colab

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

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 a…

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.

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.

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.

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.

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.

Machine Learning Models

Michigan State University Data Analytics Neural Network Challenge

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

Identifying the Digits using Image Classification

Our project utilizes machine learning models to predict cardiovascular diseases (CVDs) by analyzing diverse datasets and exploring 14 different algorithms. The aim is to enable early detection, personalized interventions, and improved healthcare outcomes.