GitHub - xxprakasa/Egier-Warehouse-Production-Trend-Analysis: This repository contains Agief Prakasa Nurdien's AoL (Assurance of Learning) project for Scientific Computing course (MATH6183001). It applies polynomial regression to analyze EGIER’s monthly production data (M1–M144) from 2018–2023 and visualize production trends for forecasting and capacity planning.

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
AOL_SC_Problem2.ipynb		AOL_SC_Problem2.ipynb
Aol Scientific Computing group 6 LQ01 - Problem 1.docx		Aol Scientific Computing group 6 LQ01 - Problem 1.docx
Aol Scientific Computing group 6 LQ01 - Problem 2.docx		Aol Scientific Computing group 6 LQ01 - Problem 2.docx
Aol Scientific Computing group 6 LQ01 - Problem 3.docx		Aol Scientific Computing group 6 LQ01 - Problem 3.docx
Aol Scientific Computing group 6 LQ01 - Problem 4.docx		Aol Scientific Computing group 6 LQ01 - Problem 4.docx
aolSC.ipynb		aolSC.ipynb
aolSClecProb3.ipynb		aolSClecProb3.ipynb
aol_data.xlsx		aol_data.xlsx

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This repository contains Agief Prakasa Nurdien's AoL (Assurance of Learning) project for Scientific Computing course (MATH6183001). It applies polynomial regression to analyze EGIER’s monthly production data (M1–M144) from 2018–2023 and visualize production trends for forecasting and capacity planning.

python data-science jupyter-notebook data-visualization scientific-computing polynomial-regression time-series-analysis trend-analysis binus-university