The objective of the project is to build an application that could predict the sales using the Walmart dataset. This application will help in providing us with the data on future sales, and hence we can improve the sales of the company. Walmart is one of the biggest retail services in the world. With 45 stores across the world, the data associated with it is huge in number.
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Requirements of the Project
The dataset can be obtained from any site such as www.kaggle.com. The dataset is usually divided into three parts, which contain train.csv, store.csv, and features.csv. The train.csv contains the historical sales data of the Walmart stores. The store.csv is the place for data comprising the type and size of the various stores located around the world. The additional data which contains information about stores, departments, products, etc. is contained in the features.csv file.
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The first step should be the merging of data from all the datasets to build a model for the application. All the unnecessary data should also be removed from all the three files during this process. We then have to categorize the data into columns, which can be done through various algorithms and methods. All the coding will be done in python language. The various inputs, such as sales place wise, sales product-wise, sales profit-wise, etc., will take the data and process it.
Here, machine learning is playing a very important role as it studies the various patterns and variations of data. The edit metadata will be very helpful in categorizing the data. The weekly sales will be predicted by using the regression model. If one didn’t get the desired output, then they can also use the boosted regression tree. The boosted regression model works in dimensionality reduction to improve the prediction of sales.
What are the methods used in this project?
There are three methods used in this project by using the algorithms, which are Random forest, gradient boosting, and extra trees. These methods can be used to classify the dataset well and play an important role in the forecasting. The boosted decision tree algorithm processes the data, and it will help to reduce the error also.
The results will be predicted efficiently and accurately if all the parameters are followed well. The benefits of this application are many; as such, it will help to track the sales ups and downs during holidays. The linear regression model can prove helpful as it predicts the sales of a particular area. The companies can track their product popularity and then work in the direction to make it more popular.
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