Demand forecasting is a key component to every growing online business. Without proper demand forecasting processes in place,it can be nearly impossible to have the right amount of stock on hand at any given time. A food delivery service has to dealwith a lot of perishable raw materials which makes it all the more important for such a company to accurately forecast daily and weekly demand. Too much invertory 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. In this challenge, get a taste of demand forecasting challenge using a real datasets.
We have developed a Machine Learning Model for predicting the future demands for specific restaurents using previous weeks(0-145) datas and we made inventory management for specific food industry and we added blockchain technology for tracing the food we made it.We used Flask framwork for developing web and Firebase for database.
1.Historical data of demand for a product-center combination(Weeks:1 to 145)
2.Product(Meal) features such as category,sub-category,current price and discount
3.Information for fulfillment center like center area, city information etc.
Dataset Collected from kaggle link of dataset
We have made a Machine Learning model by using GradientBoosting algorithm which has accuracy of about 93-95% for training data and 83% for testing data.
.png)
.png)
.png)
.png)
.png)
pip3 install flask pyrebase numpy pickle
python app.py
export FLASK_APP=block_chain.py
flask run --port 8000
- SANJAI - Backend end and Machine Learning Part Github Link
- Madumitha- Frontend HTML/CSS