Supply Chain Shipment Price Prediction
Code 🧑💻
1. Problem
We have been provided with some supply chain data, our task is to determine and predict the price of the product including the shipping cost.
Further, we have to use a variety of ML algorithms to make our predictions to derive which algorithm suits best for us.
2. Data
The dataset we're using is from Kaggle's Supply Chain Shipment Price Data Analysis.
3. Solution
After analyzing the problem I broke it into 10 smaller steps which would help in the derivation of my desired results.
- Imported all the useful libraries
- Uploaded CSV files
- Performed some EDA and got some insights into the data
- Dropped, Cleaned, Encoded, and did some useful Feature Engineering
- Started preprocessing within a function ‘preprocess_inputs()’
- Applied standardization in all respective columns’ values
- Split the dataset into Train and Test data
- Applied Classical ML algorithms such as Linear Regression, Decision Tree, Random Forest
- Chose the (r^2) score and adjusted the (r^2) score for filtering out the best method.
- In the end, Random Forest Regressor emerged as the best fit for the given problem.