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Deep-dived into data processing techniques to analyze sales esteem for a brand against cities and developed a prediction model using ML to suggest trends to the company

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CAPSTONE-PROJECT-ON-RETAIL-ANALYTICS

Deep-dived into data processing techniques to analyze sales esteem for a brand against cities and developed a prediction model using ML to suggest trends to the company.

1.Business Problem Understanding: The sales of each brand in amazon differs by order state, date, and other features. There are states where some brands have no sales and some brands find difficulties while selling their products and there are states where particular products are highly purchased too.

2.Business Objective: The objective is to device a machine learning model to predict the total payment for each brand. The objective is to device a model to group the products based on their brands and the locality where they have been sold and prioritize the products based on the sales. Our objective is to find out which brands products is getting high sales.

3.Approach: Our approach to the business problem involves Data understanding, Data Pre-processing, and Exploratory data Analysis (EDA) on the data that we have obtained, and understanding the dependency of features in the prediction of price and segmentation basis on brands and locality.

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Deep-dived into data processing techniques to analyze sales esteem for a brand against cities and developed a prediction model using ML to suggest trends to the company

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