WALMART SALES PREDICTION USING TREE AND RANDOM FOREST

SERA, CLEMENTINO ENDRICOUES KEDA (2023) WALMART SALES PREDICTION USING TREE AND RANDOM FOREST. Other thesis, Universitas Katholik Soegijapranata Semarang.

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17.K1.0043-CLEMENTINO ENDRICOUES KEDA SERA-COVER_a.pdf
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Abstract

Products sold in stores are human needs for everyday driving. The business is facing a challenge due to unforeseen demands and runs out of stock sometimes. The collected data will be processed using the Tree and Random Forest algorithm which is processed in the Orange application to predict weekly sales at Walmart. In the Tree and Random Forest, after calculating the data that has been obtained from each of these algorithms, the data is compared with each algorithm to see which score is good. From the test results, the RMSE value for the Random Forest model is 157442 and the Tree is 189694. From these results, it can be concluded that Random Forest is better in terms of testing.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: mr AM. Pudja Adjie Sudoso
Date Deposited: 04 Apr 2023 06:01
Last Modified: 04 Apr 2023 06:01
URI: http://repository.unika.ac.id/id/eprint/31395

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