README.md
README.md
Shipment price predictive forecasting uses statistical techniques and machine learning to predict future prices based on historical data. It assists in understanding past trends, enhancing decision-making for cash flow, risk assessment, capacity planning, and meeting customer demands in the supply chain.
Problem Statement:
To provide a methodological approach to analyze the ongoing trends and predicting the future price of shipment packages based on various factors which affect the pricing. This prediction is to be done using the machine learning models.
In this project we have used three different regression models and they are:-
a) Linear Regression.
b) Random Forest Regressor.
c) LGBM Regressor.
Based on the data and requirements of the supply chain domain we developed parameters for predicting the freight cost for each shipment. To get more accurate results we have tried these three algorithms and out if these LGBM Regressor has given us the best results.