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

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

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

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.

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.