Sarvesh Kumar Sharma's Projects
HackeRank 30 days of code challenge Solution implemented in Python, Java, and C Language.
An effective Agric-E-commerce solution where people can buy fresh agricultural products directly from the farmers and let farmers sell their products without paying extra charges in between as well as grow up their market outside of the locality.
the backend of Mini project AwareWeGo Created using ExpressJs, NodeJs, and NoSql Database MongoDB (connect to mongodb atlas)
Cardio Monitor is a web app that helps you to find out whether you are at risk of developing heart disease. the model used for prediction has an accuracy of 92%. This is the course project of subject Big Data Analytics (BCSE0158).
This is the source code of article how to create a chatbot in python . i.e A chatbot using the Reformer, also known as the efficient Transformer, to generate dialogues between two bots.
This Deep Learning Project aims to provide colorizing black & white images with Python. In image colorization, we take a black and white image as input and produce a colored image. We will solve this project with OpenCV deep neural network.
A CRUD API for a contact book flask app created using pymongo python and flask.
The capstone project of IBM Data Science professional certificate course offered by Coursera
A collection of data analysis and visualization projects designed to uncover insights from diverse datasets. These projects include analyses on COVID-19 trends, stock trading patterns, housing market prices, IoT data, and more, showcasing the power of data-driven storytelling.
My Assignment work on Data-Analysis, Data-Visualization, and Machine learning Algorithms.
This repository includes different Data-Structures and Algorithms performed in python for the course-Design and Analysis of Algorithms.
In this repository, I will keep my all Deep Learning project implementations.
In this, you are given driver images, each taken in a car with a driver doing something in the car (texting, eating, talking on the phone, makeup, reaching behind, etc). Your goal is to predict the likelihood of what the driver is doing in each picture. The 10 classes to predict are as follows, c0: safe driving c1: texting - right c2: talking on the phone - right c3: texting - left c4: talking on the phone - left c5: operating the radio c6: drinking c7: reaching behind c8: hair and makeup c9: talking to passenger
A web development project of an E-commerce site using HTML ,CSS Bootstrap and PHP with mysqli.
Front end web development practicals and Implementations from scratch.
This repository contains all the Practical assignments of course subject full stack using NodeJS. The web technologies include MongoDB, ReactJS, ExpressJS and NodeJS
The solutions of Hackerrank tutorial '10 days of Statistics ' implemented in python.
Hackerrank Solve C Practice Questions Solutions
This experiment is the classification of human activities using a 2D pose time series dataset and an LSTM RNN. The idea is to prove the concept that using a series of 2D poses, rather than 3D poses or a raw 2D images, can produce an accurate estimation of the behaviour of a person or animal.
Image Data Augmentation with Keras and Image data generator with keras model.
Lane Line detection is a critical component for self driving cars and also for computer vision in general. This concept is used to describe the path for self-driving cars and to avoid the risk of getting in another lane. In this repo, we will build a machine learning project to detect lane lines in real-time. We will do this using the concepts of computer vision using OpenCV library. To detect the lane we have to detect the white markings on both sides on the lane.
LinkedList Implementation and solutions of different problems related to linkedlist taken from Hackerrank and Leetcode.
This repository contains machine learning Courses that I undertook from Online Platforms Coursera .
This repository showcases a selection of machine learning projects undertaken to understand and master various ML concepts. Each project reflects commitment to applying theoretical knowledge to practical scenarios, demonstrating proficiency in machine learning techniques and tools.
The task is to use the training dataset to develop an algorithm that automatically labels each case in the test set as one or more MoA classes. Note that since drugs can have multiple MoA annotations, the task is formally a multi-label classification problem.
This Repository stores Lab-works of Machine learning and its application course.
Final Practical of Course subject Full Stack using Scripting technologies.it includes designing template number 25.
This is based on a data challenge from the Michigan Data Science Team (MDST). The Michigan Data Science Team (MDST) and the Michigan Student Symposium for Interdisciplinary Statistical Sciences (MSSISS) have partnered with the City of Detroit to help solve one of the most pressing problems facing Detroit - blight. Blight violations are issued by the city to individuals who allow their properties to remain in a deteriorated condition. Every year, the city of Detroit issues millions of dollars in fines to residents and every year, many of these fines remain unpaid. Enforcing unpaid blight fines is a costly and tedious process, so the city wants to know: how can we increase blight ticket compliance?
Machine learning project on Distinguish between the presence and absence of cardiac arrhythmia and its classification in one of the 16 groups.