AI & Machine Learning is creating a big boom in every type of industry. It dramatically helps many peoples by assisting or recommending them with essential things, and it is like a personal assistant in all the aspects. So the next phase of the revolution is wholly based on the AI, Robotic and IoT. In Demand Forecasting, Machine Learning prediction helps in predicting the future demands of the product, and it will help the Supply Chain sectors and Product Manufacturers to plan their production in accordingly, So it helps in making a more profitable business. I read some articles related to the demand forecasting Gather all the information related to demand forecasting. Go through problem description thoroughly Understand the overview of the problem. Acquire the datasets from Kaggle (Historical Product Demand) which contains 4 Independent features and 1 Dependent feature So, this is taken as a Supervised learning method.
Independent Feature: Product_Code Warehouse Product_Category Dated Dependent Feature: Order_Demand Working: The first phase is the cleaning of the dataset, where features are grouped into Object Datatype.’ After that, we will convert the features to an Integer type, EDA, Feature Engineering. Next, we will deploy in Linear Regression, XGBoost Regression model with the help of Grid Search CV for hyper-parameter tuning and check for the accuracy. In the last stage, we are deploying of the dataset into Time Series model also. Further, the dataset is being deployed in LSTM model to achieve the improvement inaccuracy.