This analysis delves into the fascinating world of retail data to unlock valuable insights for businesses. Market Basket Analysis is a powerful technique that allows retailers to understand customer behavior, identify item associations, and offer personalized recommendations.
Market Basket Analysis from Kaggle.
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Data Exploration, begin by loading the dataset and exploring it to understand the data's structure and characteristics.
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Handling Missing Values, identify and handle missing values appropriately to ensure data quality.
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Data Type Conversion, convert the "Date" column to a datetime format and verify the data types of other columns.
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Conduct EDA to gain insights into the dataset, including data distribution and sales trends over time.
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Create visualizations, including histograms, box plots, and bar charts, to visualize numerical and categorical variables.
- Transform data into transaction-level format using the Pandas and mlxtend libraries.
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Apply the Apriori algorithm to perform association rule mining on the transaction dataset.
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Experiment with different support and confidence thresholds to generate meaningful rules.
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Generate frequent item sets and association rules with support, confidence, and lift values.
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Assess rules based on support, confidence, and lift.
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Filter and select relevant rules based on business objectives.
- Visualize the discovered association rules using scatter plots and network graphs.
- Interpret association rules in the context of your retail business to identify actionable insights related to product relationships, customer behavior, and more.
This project showcases the power of data-driven insights in the retail industry. It empowers businesses to optimize their product catalog, enhance marketing strategies, and improve customer engagement and satisfaction.
By following these key steps, you can transform association rules into actionable insights that drive your retail business forward.