The aim is to build a predictive model and find out the sales of each product at a particular store.Using this model,We will try to understand the properties of products and stores which play a key role in increasing sales.
- Perform data cleaning on the dataset
- Make a EDA report
- Visualize the distributions of various features and correlations between them
- Feature engineering to extract the correct features for the model
- Build a regression model to predict the outlet sales
- Recommend price changes for optimising future sales
- Implement
- Selected BigMart Data kaggle dataset as it satisfies all criterions
- Used simple linear regression to achieve final results
- Designed WebApp using Flask for easier access for clients
-detail filling
-prediction results
-glimpses of trasin and test dataframes
-Example EDA of Item_Weight attribute using Dtale library
-Example EDA of Item_Weight attribute using Pandas library
-Example EDA of Item_Weight attribute using Pandas library
