The document discusses a project that uses machine learning algorithms to predict sales on Black Friday using customer data from previous years. It analyzes a dataset containing customer attributes and purchase amounts. Three regression models - Lasso, linear, and random forest regression - are trained on 80% of the data and tested on the remaining 20%. The random forest model achieved the highest prediction accuracy according to the root mean square error metric. Retailers can use these predictions to better understand customer purchasing behavior and inform marketing strategies for Black Friday.

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SALES PREDICTION ON BLACK FRIDAY DATASET USING MACHINE LEARNING
ABSTRACT
On the eve of Black Friday, goods are sold at a very high discount and thus increases the sales
around 30 folds as compared to the normal flash sale days. The customer's data those have
bought products on this day can be analyzed which will give a brief declaration of their choice
on various products. The proposed work analyzed the data containing the tuples of customers
along with their buying factors and amount. This data is analyzed and predicted using machine
learning algorithm. Three models that are Lasso regression, Linear regression, and random forest
regression models have been used for the prediction for 80:20 distribution of training and testing
data. The accuracy results are highlighted in the form of root mean square error (RMSE) score
and the accuracy of all the models in different cases have been depicted in the form of their
accuracy graphs.
Algorithm Used: Random forest, Linear regression

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PROBLEM STATEMENT
Because consumers are eager to spend so much money during this period, retailers seriously look
forward to good preparation for the shopping holiday. In preparation for this day, retailers will
typically hire more employees, stock their commodities, prepare new promotions, and decorate
store layouts. Retailers rely on designing advertising campaigns to attract more customers into
their stores and/or their online shops. In order to maximize their efforts and revenues, retailers
enthusiastically understand how the consumers make shopping decisions that will assist them to
achieve the most profits during the shopping season. Many possible parameters that have been
considered are presented. If retailers comprehensively understand their customers in terms of
characteristics, behaviors and motivations in the previous shopping seasons, they can implement
and develop more effective marketing strategies for specific customers categories.
EXISTING SYSTEM
❖ Have given full analysis of behavior on Black Friday highlighting the attributes like
occupation, marital status affecting the sales on the day of Black Friday.
❖ Explained how the visualization can affect the psychology of customers and marketing.
They have discussed about the features like environment color which can enhance
customer feeling, and purchase likelihood.
❖ Research methods combining qualitative and quantitative sampling, data collection and
analysis techniques.
DISADVANTAGES
❖ Of all the above study, we found that there is a lack of experimentation for various types
of dataset splits.

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PROPOSED SYSTEM
❖ Sales prediction on black Friday dataset is analyzed here.
❖ Machine learning algorithm like regression methods such as Lasso, Linear regression and
random forest are used
❖ Data visualization and data analysis is proposed
ADVANTAGES
❖ High accuracy of data prediction
❖ Regression found to be useful model for black Friday sales dataset.

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HARDWARE REQUIREMENTS
Processor : Any Processor above 500 MHz.
Ram : 4 GB
Hard Disk : 4 GB
Input device : Standard Keyboard and Mouse.
Output device : VGA and High Resolution Monitor.
SOFTWARE SPECIFICATION
Operating System : Windows 7 or higher
Programming : Python 3.6 and related libraries

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SYSTEM ARCHITECTURE
Figure: System Architecture
Lasso
Training set
Dataset
Random Forest
Learn model
Model
Test set
Test model
Predicted
Sales data
Analyze
Linear Regress

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The following screen shows the correlation matrix of data visualization

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The following screen shows the Histogram data visualization
The following screen shows the Evaluation metrics of Lasso