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Sales Optimizer

The retail stores always face a problem of tension between demand and supply. Sometimes they stock more products than it has demand or sales and so…

231 lines🖥 Desktop only

View source folder on GitHub →contributed by

@rik-chatterjee

Sales Optimizer

Descirption

The retail stores always face a problem of tension between demand and supply. Sometimes they stock more products than it has demand or sales and sometimes they stock less than demand, due to which they either suffer from loss or dissatisfied customers. This problem can be solved using this software. It uses various inventory data to predict the sales pattern of the retail store so that the retail store can stock their products accordingly.

Parameters Used:

Item weight
Item fat content
Item fat content
Item visibility
Item type
Item MRP
Outlet establistment year
Outlet size
Outlet location.

Libraries Used:

Numpy
Pandas
Seaborn
Scikit-learn

Example

Place sales_data.csv (containing columns such as Item_Weight, Item_Fat_Content, Item_Visibility, Item_Type, Item_MRP, Outlet_Identifier, Outlet_Establishment_Year, Outlet_Size, Outlet_Location_Type, Outlet_Type, and Item_Outlet_Sales) in the project directory.
Run python main.py. The script preprocesses the data (fills missing weights, encodes categorical columns), trains a RandomForestRegressor, and prints dataset info and evaluation metrics to the console.
Two matplotlib windows open: a seaborn pairplot of the features, and a scatter plot comparing actual vs predicted Item_Outlet_Sales values.

How to run on localhost

pip install numpy pandas seaborn scikit-learn matplotlib
python main.py