MAE presentation

VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

R visualisation of output from Viper

Wine Quality Red Test

Investigating Gradient Descent behavior in linear regression

This machine learning project focused on predicting food delivery times. The code emphasizes essential tasks such as data cleaning, feature engineering, categorical feature encoding, data splitting, and standardization to establish a solid foundation for building a robust predictive model.

time series-Jena Climate

Predicting Walmart Sales and Performing Exploratory Data Analysis

Adversarial Patch defense using SegmentAndComplete (SAC) & Masked AutoEncoder (MAE)

Time Series Forecasting Methods to forecast Daily Post Publications on Medium

Predicting turbine energy yield (TEY) using ambient variables as features.

Before GUI, There are Two ways to preprocessing any data set with two jupyter notebooks, GUI to choose Cleaned CSV data_set,Show most of properties of this data_set,Choose test size & alpha size & error metrics to train Ml algorithm on this data set,show ( test & train ) Percentage as output

Templates of statistical and DL forecasting on some synthetic data and lastly predicting sunspot using kaggle sunspot dataset.

Implementation of a simple linear regression with single feature

Regression algorithms to predict the median house prices in California districts

A git repo to help me track changes in harder MATLAB coding questions

This repository contains my attempt for ZS Data Challenge 2019. The challenge is based on Football Legend Cristiano Ronaldo.

Trying to forecast CocaCola Sales for the future quarters by analyzing data from previous quarters of 10 years.