store sales prediction

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  sales dependency on various factors

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  sales plot

Inspiration

Sales prediction is an important part of modern business intelligence. Many influencers indulge in digital marketing and be a part of various product promotion. Basically this project outlays a sales graph of the product store. It can be a complex problem, especially in the case of lack of data, missing data, and the presence of outliers. Machine-learning algorithms make it possible to find patterns in the time series. We can find complicated patterns in the sales dynamics, using supervised machine-learning methods.

What it does

The dataset would scale with the number of customers on that day. Thus by plotting the trend, it is able to confirm that there was a correlation between customers and sales. Continuing with this insight, it is able to separate them by the day of week, confirming that some days were correlating more strongly than others.

How we built it

the use of regression approaches can often give us better results compared to time series methods. Machine-learning algorithms make it possible to find patterns in the time series. We can find complicated patterns in the sales dynamics, using supervised machine-learning methods. Some of the most popular are tree-based machine-learning algorithms. We have implemented normal regression techniques and as well as boosting techniques in our approach and have found that the boosting algorithms have better results than the regular regression algorithms. By continuing using the ensemble methods such as Adaboost and Random Forest, which gave an improvement on the validation set.

Challenges we ran into

Implementing the training models was a bit challenging task where in it needed to consider various parameters . The dataset itself a quite complex task to find.

Accomplishments that we're proud of

Successful implementation of the idea Plotting the sales graph after running through the trained models.

What we learned

A general overview of how sales is dependent on various parameters such as customers, holidays, competition stores etc. Implementing a machine learning model for calculating sales and predicting sales for the store dataset.

What's next for store sales prediction

The model can be made more accurate by increasing the dataset. This project can have an interactive user interface and can be made more user friendly.

Built With

• machine-learning
• python