Sales Price Prediction - Readme

Description

The Sales Price Prediction project utilizes machine learning algorithms to predict product prices based on various features. The dataset includes the following features:

1. Item_Identifier: Unique product ID
2. Item_Weight: Weight of the product
3. Item_Fat_Content: Whether the product is low fat or not
4. Item_Visibility: The % of total display area allocated to the product
5. Item_Type: The category of the product
6. Item_MRP: Maximum Retail Price of the product
7. Outlet_Identifier: Unique store ID
8. Outlet_Establishment_Year: The year the store was established
9. Outlet_Size: The size of the store in terms of ground area
10. Outlet_Location_Type: The type of city where the store is located
11. Outlet_Type: Type of outlet (grocery store or supermarket)
12. Item_Outlet_Sales: Sales of the product in the store (outcome variable)

Deployment

The model has been deployed using Streamlit, offering a user-friendly web interface for predicting sales prices. Users can input product details, and the model will provide sales price predictions based on the trained data.

How to Use

1. App link Streamlit prediction app

Data Source

The dataset used for this project can be found at kaggle bigmart data.

Note

This project is for educational and demonstration purposes only. The predictions may not reflect actual market conditions.