- Introduction
- This project aims to perform a comprehensive analysis of Adidas sales in the United States. Utilising data-driven insights, the project seeks to understand sales trends, customer preferences, and regional performance.
- The analysis is encapsulated in a Jupyter notebook titled Adidas_sales_Analysis.ipynb, which explores various facets of the sales data, ranging from exploratory data analysis to predictive modelling.
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Dataset Description The dataset, Adidas_US_Sales_Datasets.csv, comprises 9648 entries spanning several key metrics:
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Retailer Information: Includes retailer name and a unique retailer ID.
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Sale Details: Contains the invoice date, product type (e.g., Men's Street Footwear), units sold, and total sales value.
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Financial Metrics: Provides data on price per unit, operating profit, and operating margin.
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Geographical Information: Covers the region, state, and city of each sale.
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Sales Method: Indicates whether the sale was in-store or another method.
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Analysis Overview The Jupyter notebook conducts a meticulous analysis in several stages:
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Data Cleaning and Preprocessing: Initial stage focusing on preparing the data for analysis by handling missing values and refining data types.
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Exploratory Data Analysis (EDA): In-depth exploration of the dataset through various visualisations, aiming to uncover trends and patterns.
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Sales Trend Analysis: Examination of sales over time to identify seasonal trends and other temporal patterns.
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Predictive Modelling: Implementation of forecasting models like ARIMA to predict future sales trends.
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Results and Conclusions The analysis yields valuable insights into Adidas' sales dynamics in the US market. Key findings include:
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Seasonal trends and their impact on different product categories.
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Performance analysis of various regions and states in terms of sales volume and profitability.
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Predictive forecasts providing a future outlook on sales trends.
The project concludes with actionable recommendations based on the insights derived from the data, potentially guiding strategic decisions for Adidas' sales and marketing teams.
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Usage To delve into the analysis, open the Adidas_sales_Analysis.ipynb notebook in a Jupyter environment. Ensure all dependencies are installed as specified in the notebook. The dataset Adidas_US_Sales_Datasets.csv should be placed in the same directory as the notebook for seamless integration.
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Contributions and Feedback Feedback and contributions to this project are highly welcome. Please feel free to suggest improvements or additional analyses that could enhance the understanding of the sales data.
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Licence This project is released under the MIT License. For more details, please refer to the LICENSE file.