BlackFriday

Building a model for prediction on Black Friday sales dataset

Overview:

A retail company “ABC Private Limited” wants to understand the customer purchase behavior (specifically, purchase amount) against various products of different categories. They have shared purchase summary of various customers for selected high volume products from last month. The data set also contains customer demographics (age, gender, marital status, city_type, stay_in_current_city), product details (product_id and product category) and Total purchase_amount from last month.

Dataset Information:

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

Libaries uses:

• Numpy
• Pandas
• Sckit-learn
• pandas-profiling
• Seaborn
• matplotlib

ScreenShot Of Application:

• Taking input from user

• Output of the given data