BackOrder prdiction. using Machine Learning. Deployed link = https://backorderprediction.herokuapp.com/
To predict BackOrder prdiction using Machine Learning. Backorders are unavoidable, but by anticipating which things will be backordered, planning can be streamlined at several levels, preventing unexpected strain on production, logistics, and transportation. ERP systems generate a lot of data (mainly structured) and also contain a lot of historical data; if this data can be properly utilized, a predictive model to forecast backorders and plan accordingly can be constructed. Based on past data from inventories, supply chain, and sales, classify the products as going into backorder (Yes or No).
The classical machine learning tasks like Data Exploration, Data Cleaning, Feature Engineering, Model Building and Model Testing. Try out different machine learning algorithms that’s best fit for the above case.
I started exploring datasets using pandas, NumPy,matplotlib and seaborn.
checking null values, checking outliers, checking imbalance in dataset.
Ploted colleration matrix to get insights about dependend and independed variables. making bar graphs, box plot, scatter plot, etc.
Made many Models(linear regression, svm, xgboost, Random Forest). But selected RandomForest Regressor.
As per selected trained model is dumped to pickled format for app development
Pycharm
Using Flask for making UI. Database = Casendra_database
heroku
https://backorderprediction.herokuapp.com/