A machine learning model developed using XGBoost to optimize supply chain parameters such as reorder quantity and frequency. Conducted data cleaning, exploratory data analysis (EDA), and feature engineering. Evaluated model performance using various metrics and benchmarked against alternative models to ensure optimal results.
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A machine learning model developed using XGBoost to optimize supply chain parameters such as reorder quantity and frequency. Conducted data cleaning, exploratory data analysis (EDA), and feature engineering. Evaluated model performance using various metrics and benchmarked against alternative models to ensure optimal results.
khushl21/supply-chain-optimization
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A machine learning model developed using XGBoost to optimize supply chain parameters such as reorder quantity and frequency. Conducted data cleaning, exploratory data analysis (EDA), and feature engineering. Evaluated model performance using various metrics and benchmarked against alternative models to ensure optimal results.
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