Food Demand Forecasting for Food Delivery Company using IBM Cloud
A food delivery service has to deal with a lot of perishable raw materials which makes it all, the most important factor for such a company is to accurately forecast daily and weekly demand. Too much inventory in the warehouse means more risk of wastage, and not enough could lead to out-of-stocks - and push customers to seek solutions from your competitors.
The main aim of this project is to create an appropriate machine learning model to forecast then number of orders to gather raw materials for next ten weeks. To achieve this, we should know the information about of fulfilment center like area, city etc., and meal information like category of food sub category of food price of the food or discount in particular week.
Software requirements of the project:
1. IBM Cognos Analytics
2. IBM Cloud
3. Jpyter Notebook
4. Anaconda
5. Kaggle Dataset
6. VS Code
While working on the project, we got to know many trending trends in the market. The working predicting model gives a different insights of the demand of perisable food items. The correct requirement plays a vital role in the emerging market.
There is multiple applications of the proposed solution:
- The company will make a lot of profit.
- The company will be able to optimize the right resources at right time.
- It will also help the customer to get it's required items as per their requirements.
The main moto is to save the wastage of food. It takes a long time and effort for the farmers to grow the food raw materials. The availability of the food items make the society better. Our proposed model will definitely add values to the company to provide and serve it's valuable customer and make a strong position while competiting with the different competitors.
https://drive.google.com/file/d/1ywfz_aDpjRvYWubI6-evyLTc5fnDavPG/view?usp=sharing