Ahmed, TanvirFahad, Sheikh AbdulTrina Akand, Takowa IslamAhammed Raju, Md. Reaj UddinYadav, Ranjita2023-04-132023-04-1320222022-09ID: 18101462ID: 17101333ID: 17101385ID: 18201202http://hdl.handle.net/10361/18149Cataloged from PDF version of thesis.Includes bibliographical references (pages 38-40).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.Global human life is significantly impacted by weather forecasts. It takes a lot of computing resources to solve mathematical equations that are based on climatic circumstances. As a result, during the past ten years, deep learning algorithms have been integrated with enormous amounts of weather observation data. At the moment, a lot of data is being consumed. So, we can increase the accuracy of weather forecasts by combining this enormous amount of data with deep learning methods. In this paper, we implement benchmark datasets for autoencoder and linear regression. We are using z500 dataset, temp 2m dataset and t850 data set. As training the linear regression on the full data will take a lot of memory which is why we took every 5th time step that almost give the same result. Using a linear regression approach and an auto encoder model, we trained and obtained day-level predictions using the ERA5 reanalysis dataset (Hersbach et al., 2020) to determine the accuracy of the test data and training data.40 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Deep learningWeather forecastingLinear regressionAuto encoderPredictionde-noiseLinear regression analysisMachine learning.Machine learning--Statistical methods--Congresses.Thesis