"Experimental Study of the Steel Market Through CNN-LSTM Deep Learning Models: Practical Applications for Cost Reduction in Industries"
This repository contains the most important code notebooks and models used for my experimental study of the steel market and practical industrial applications, entitled "Experimental Study of the Steel Market Through CNN-LSTM Deep Learning Models: Practical Applications for Cost Reduction in Industries." This study was conducted as part of my undergraduate thesis in industrial engineering.
The study combines an in-depth industrial and economic analysis of the steel market with experiments testing the feasibility of implementing these DL models in the purchasing decisions of industries (such as manufacturing, automotive, shipbuilding, aerospace, etc.) that use steel as an intermediate product to produce finished goods.
The conclusions of the study are not included here, nor are some less important notebooks in which additional analyses were conducted or other graphs were created.
Note: The names of the folders used on the local device have been changed in the uploaded code. The study data was obtained from third-party data providers; however, for practical reasons, I have used Yahoo Finance data in this code.