Bigmart is a vast supermarket chain which is located nearly at every megacity. The sales of Bigmart are very crucial, and data scientists study those patterns per product and per store to decide about the new centers. Using machine learning to predict Bigmart sales enables the data scientist to do so, as it studies the various patterns per store and per product to give accurate results.
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To build this application, various machine learning aspects are used, such as Supervised Learning task, Regression task, and Plain Batch learning. Supervised learning will help you to understand the flow of data and knowing the sale prices, etc. The regression task uses algorithms to predict sales prices. Batch learning will help you to study the data in batches and improvised the results.
What should be considered for the project?
The sales are to be kept in mind to predict the results, and the sales depend on the location of the store, population around the store, brand popularity, etc. One should also know about the city in which the store is located, either it is in an urban area or rural. Population statistics around the store also affect the sales, then store capacity should also be considered, and many more things.
The brands which are being sold at the Bigmart also play an essential role in predicting sales. The product sallies vary through the utility of the product, display area, advertising of the product, and many more aspects. The data set is quite big, and it needs to be decoded through algorithms.
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The first step will be declaring variables that will do the calculations of data. The variables should be declared for Item visibility, Item type, Outlet size, Outlet location type, Outlet type, and Item outlet sales. The data is categorized, and the first step will be to the correction of irregularities through data pre-processing. The variation of data is a real tough task as there are around 1562 unique items in a single store.
The second step is to combine the outlet type through various parameters such as item visibility, years of operation, etc. Then create a broad category for item type using many item identifiers. Then the algorithm of ML will study the variations. A generic function that makes the model and performs cross-validation should be made.
The next step will be the model making of the application, which will comprise the linear regression model, ridge regression model, decision tree model to decide the results, etc. The data fed to the application will go through sorting and arrangements which will be efficiently performed by Machine Learning.
The algorithm which performs the best in the application is the XGBoost, which arranges the data and studies it for its variations. The results predicted will be very useful for the executives of the company to know about their sales and profits. This will also give them the idea for their new locations or centers of Bigmart.
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