Black Friday Sales Prediction

Building a regression model for prediction on Black Friday Dataset

Overview:

• In this project, we are going to predict the purchasing amount of customers during Black Friday, using various features such as age, gender, marital status.
• We will follow all the steps of a Data Science lifecycle from data collection to model deployment.

Read More about it in my Blogpost

WebApp:

Link: https://black-friday-sales-prediction.herokuapp.com/

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Dataset:

Column ID	Column Name	Data type	Description	Masked
0	User_ID	int64	Unique Id of customer	False
1	Product_ID	object	Unique Id of product	False
2	Gender	object	Sex of customer	False
3	Age	object	Age of customer	False
4	Occupation	int64	Occupation code of customer	True
5	City_Category	object	City of customer	True
6	Stay_In_Current_City_Years	object	Number of years of stay in city	False
7	Marital_Status	int64	Marital status of customer	False
8	Product_Category_1	int64	Category of product	True
9	Product_Category_2	float64	Category of product	True
10	Product_Category_3	float64	Category of product	True
11	Purchase	int64	Purchase amount	False

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Motivation:

• Predicting customer's behaviour is one of the most popular applications of Machine Learning in various fields like Finance, Sales, Marketing.
• Building such predictive models, we can predict the impact of the decisions taken on the growth of our organization.

Conclusion:

• In this project, we tried to build a model using various algorithms such as Linear regression, KNN regression, Decision tree regression, Random forest and XGB regressor to get the best possible prediction.
• The hyperparameter tuned XGB regressor gives us the best rmse value and r2 score for this problem.