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Sales prediction on black friday dataset using machine learning

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Sales prediction on black friday dataset using machine learning

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DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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
DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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.
DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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.
DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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
DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com SYSTEM ARCHITECTURE Figure: System Architecture Lasso Training set Dataset Random Forest Learn model Model Test set Test model Predicted Sales data Analyze Linear Regress
DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com The following screen shows the correlation matrix of data visualization
DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com The following screen shows the Histogram data visualization The following screen shows the Evaluation metrics of Lasso

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Sales prediction on black friday dataset using machine learning

  • 1. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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
  • 2. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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.
  • 3. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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.
  • 4. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com 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
  • 5. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com SYSTEM ARCHITECTURE Figure: System Architecture Lasso Training set Dataset Random Forest Learn model Model Test set Test model Predicted Sales data Analyze Linear Regress
  • 6. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com The following screen shows the correlation matrix of data visualization
  • 7. DATA ALCOTT SYSTEMS Call/Whatsapp 9600095046 check demo: www.finalsemprojects.com The following screen shows the Histogram data visualization The following screen shows the Evaluation metrics of Lasso