Discover @yatharthrajput Open Source projects

Yatharth Rajput (@yatharthrajput)

yatharthrajput

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Registered over 4 years ago
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Java
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Location 🇮🇳 India
Country Total Rank 1,822
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Java
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Python
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Youtube_Player

Movie_app

HackAndroid

Pentesting App for Android

razor-pay

GoldenRatio

This code fill find out the golden ratio of your face.Hope its 1.6

Laws_Of_India

This is a legal advisory app build under the org CriTax-Corp.This app will help law students, advocates and judges.All the bareacts and rules is mentioned in this app

AirML

CaptchaGenrator

Since we are using alot of AI technology.We need to verify this by using repactcha,this code generate Infinite number of captcha

Jupyter Notebook

Reminder

This app will remind you of any task by giving the notification at particular time which is fixed

MedocracyApp

BMI

BMI Calculator 💪 Our Goal The objective of this tutorial is to look at how we can customise Flutter Widgets to achieve our own beautiful user interface designs. If you have a designer on board, no matter how unconventional their designs are, we can create them using Flutter. What you will create We’re going to make a Body Mass Index Calculator inspired by the beautiful designs made by Ruben Vaalt. It will be a multi screen app with simple functionality but full-on custom styling. Finished App

Sentiment_Analysis

Sentimental Analysis using NLP

Jupyter Notebook

yatharthrajput

pwned

Pwned is a simple command-line python script to check if you have a password that has been compromised in a data breach.

Jupyter Notebook

Numpy-100_Solutions

Snapchat-Pizza_Game

Snapchat introduces a new game in which 2 player can play a game in which we have to eat the last piece of the pizza.You can play this game with computer and lets see who wins!!

Jupyter Notebook

Handwritten-Digits-Generation_DCGAN

Jupyter Notebook

GAN-for-handwritten_digits

Jupyter Notebook

BlackJack

BlackJack the one of yhe famous game which is used to play in casio.With the same logic i build this algo where you can play black jack with the computer as dealer

Jupyter Notebook

LyricsGenrator

This is a python script which guess the word which can best suited for the song from a specific dictionary of word.

Jupyter Notebook

LeetCode-problems

Here you can see the solutions of the question which are in leetcode

Jupyter Notebook

cowin

As you know it is almost impossible to book a slot for vaccine.This app will help to finding the slots of covid vaccine.You can search the vaccination centres all over the india. Pincode feature will help you to give exact location of centre

sudoko

It i a game used to solve the problem by logically placing the numbers in 9x9 sqaure.We used to wait for newspaper for new sudoko but this python script will genrate n number of sudoko with their answers.Moreover If u want to sovle a particaler sudoko u can also find the answers of them.

Jupyter Notebook

Pose-Estimation

Mr_Robot_password_genrator

A Common User Passwords generator script that looks like the tool(elpscrk) that Eliot used it in Mr.Robot xD

python-practice

This module contains all the basics of python and some code for web scraping too

Jupyter Notebook

Sudoko

It i a game used to solve the problem by logically placing the numbers in 9x9 sqaure.We used to wait for newspaper for new sudoko but this python script will genrate n number of sudoko with their answers.Moreover If u want to sovle a particaler sudoko u can also find the answers of them.

Jupyter Notebook

ChatBot

A bot which can msg you every morning,you just have to save the list of numbers and then he pinged all the selected contacts

Jupyter Notebook

encoding-decoding

Military or secret service used to send msg in the encrypted form with a special key and you can decrypt that msg with this key only.I made this program which has 1000 keys to encrypt your data so this gives a wide range to select in what manner you have to encrypt the data.

Depression-Prediction-Model

detect if u have D or not

Jupyter Notebook

Intro-to-ML

This repo will give you a brief idea about the lib used in ML. <br>1.Numpy<br> 2.Matplotlib <br>3.Pandas

Jupyter Notebook

TSF-GRIP-Data-and-Analytics-Task

TSF-GRIP-Data-and-Analytics-Task-6

Jupyter Notebook

Stock-Prediction-using-ARIMA

ARIMA (Auto Regressive Integrated Moving Average) is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values. ARIMA models explain a time series based on its own past values, basically its own lags and the lagged forecast errors. An ARIMA model is characterized by 3 terms p, d, q: - p is the order of the AR term - d is the number of differencing required to make the time series stationary - q is the order of the MA term As we see in the parameters required by the model, any stationary time series can be modeled with ARIMA models. Let's explain the term Auto Regressive in ARIMA. It means the model is a linear regression that uses its own lags as predictors. Linear regression models, as we know, work best when the predictors are independent of each other. Otherwise we run into multicollinearity issues where the regression becomes unstable due to correlation. Now most price series are non stationary otherwise we would all be rich by just buying low and selling high, waiting for the prices to mean revert. So in order to make ARIMA models work we need to difference it, in this case to compute the returns as they usually randomly distribute around a 0 mean. So we need to simply subtract the previous value from the current value. Now if we just difference once, we might not get a stationary series so we might need to do that multiple times. And the minimum number of differencing operations needed to make the series stationary needs to be imputed into our ARIMA model. If the time series is already stationary, then d is 0. But in stock price forecasting it's almost never 0. p is the order of the Auto Regressive (AR) term. It refers to the number of lags to be used as predictors. q is the order of the Moving Average (MA) term. It refers to the number of lagged forecast errors that should go into the ARIMA Model. We'll use the Augmented Dickey Fuller (ADF) test to check if the price series is stationary. The null hypothesis of the ADF test is that the time series is non-stationary. So, if the p-value of the test is less than the significance level (0.05) then we can reject the null hypothesis and infer that the time series is indeed stationary. So, in our case, if the p-value is greater than 0.05 we'll need to find the order of differencing.

Jupyter Notebook