shsarv

Machine-Learning-Projects

This Contain Some Machine Learning Projects that I have done while understanding ML Concepts.

Data-Analytics-Projects-in-python

Exploratory data analysis projects on Different datasets to enhance Data Analysis and visualization

TravelYaari-react

This project is based on how an effective travel website will make us feel. It is based on a traveling website with fully-featured functions that will activate the traveling bug with vibrant imagery. This website contains highlights of some important places along with high-quality photography and allows people to book their dream destination within their budgets. It also includes full customer support, an easy payment system, book as per your time choice, full virtual tour of place through different videos and images.

TravelYaari

This is the Repository for the Full Stack mini Project using Front End frameworks and Technologies.

AwareWeGO

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.

Restaurant-Recommendation-System

This repository contains code for the Recommendation system to find restaurants. An End to End Project developed using Flask and python. The website is hosted on Heroku.

Cardio-Monitor

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).

Project-Arrhythmia

Machine learning project on Distinguish between the presence and absence of cardiac arrhythmia and its classification in one of the 16 groups.

30-days-of-code

HackeRank 30 days of code challenge Solution implemented in Python, Java, and C Language.

ChatBot

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.

TravelYaari-Api

the backend of TravelYaari Created using ExpressJs, NodeJs, and NoSql Database MongoDB (connect to MongoDB Atlas)

ContactBookAPI

A CRUD API for a contact book flask app created using pymongo python and flask.

Coursera_capstone

The capstone project of IBM Data Science professional certificate course offered by Coursera

E-Store

A web development project of an E-commerce site using HTML ,CSS Bootstrap and PHP with mysqli.

Full-Stack-1

Front end web development practicals and Implementations from scratch.

linkedlist-with-java

LinkedList Implementation and solutions of different problems related to linkedlist taken from Hackerrank and Leetcode.

ML-and-its-Application

This Repository stores Lab-works of Machine learning and its application course.

Predicting-Property-Maintenance-Fines

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?

AwareWeGo-Api

the backend of Mini project AwareWeGo Created using ExpressJs, NodeJs, and NoSql Database MongoDB (connect to mongodb atlas)

Distracted-Driver-Detection

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

Human-Activity-Detection

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.

Research-topic-Prediction

Given the abstract and title for a set of research articles, predict the topics for each article included in the test set.

shsarv

this is a Template repository whose Readme.MD page will appear on my Github public profile.

TimeSeries-Multi-StoreSales

In this, We will work with a challenging time-series dataset consisting of daily sales data, kindly provided by one of the largest Russian software firms - 1C Company.

Colorize-Black-White-Image

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.

FullStack-2

This repository contains all the Practical assignments of course subject full stack using NodeJS. The web technologies include MongoDB, ReactJS, ExpressJS and NodeJS

Lane-Line-Detection

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.

Mechanisms-Of-Action-MoA-Prediction-Challenge

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.

practical-final

Final Practical of Course subject Full Stack using Scripting technologies.it includes designing template number 25.

R-lab