The market for supply chain analytics is expected to develop at a CAGR of 17.3 percent from 2019 to 2024, more than doubling in size. This data demonstrates how supply chain organizations are understanding the advantages of being able to predict what will happen in the future with a decent degree of certainty. Supply chain leaders may use this data to address supply chain difficulties, cut costs, and enhance service levels all at the same time. The main goal is to predict the supply chain shipment pricing based on the available factors in the dataset. The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building, Model Testing done using the Pipelines. Dashboard is fomed using the data in the excel for the further detailed study.
During the visualisation in Python, few questions are answered like Which Country and Brand has the maximum Unit Price, Which Dosage Form has maximum Pack Price or maximum shipped in terms of price etc.
Pandas, Numpy, Matplotlib, Seaborn, Plotly
Python
Jupyter Notebook
Label Encoder, Column Transformer, Grid Search CV, Linear regressor, Decision Tree, Random Forests, XGB Regressor, Select K Best, Pipelines