**Supply Chain Shipment Price Prediction**

**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.