Grocery Product Price Analysis for Australian States
This repository contains an analysis of grocery product prices for Australian states. The analysis aims to provide insights into pricing patterns, trends, and variations within the grocery product market in Australia.
Dataset The analysis is based on the "Grocery Product Prices for Australian States" dataset, available on Kaggle ( https://www.kaggle.com/datasets/thedevastator/grocery-product-prices-for-australian-states). The dataset includes information on product prices, categories, subcategories, brands, and geographical locations.
Analysis Highlights The analysis covers several key aspects:
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Descriptive Statistics: Descriptive statistics were calculated for numerical variables such as package price and unit price. This provided an overview of the price distribution and range. Categorical Analysis: Categorical analysis was performed on category, subcategory, product group, brand, state, and city columns. The frequency of each category was calculated and visualized using bar charts, offering insights into the distribution of products across different categories, subcategories, brands, and locations.
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Price Analysis: Price analysis involved comparing package prices and unit prices. Average and median prices were calculated for different categories, subcategories, and brands, enabling the identification of pricing patterns and variations. Trend Analysis: Trend analysis focused on examining price trends over time. Line plots were used to visualize the average package prices and unit prices, helping identify any notable trends or fluctuations.
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Bivariate Analysis: Bivariate analysis explored the relationship between package price and unit price. r groceries in Australia. Average prices were calculated for different states and cities, enabling comparisons and highlighting regions with relatively higher or lower prices.
Conclusion The analysis sheds light on the grocery product pricing landscape in Australia. It provides valuable insights for consumers, retailers, and policymakers to better understand market dynamics, make informed decisions related to grocery shopping, and devise pricing strategies. The findings can contribute to enhanced cost management, improved market competitiveness, and more informed consumer choices.
For a detailed analysis and code implementation, please refer to the Jupyter Notebook provided in this repository.
Note: The analysis is based on the available dataset and the interpretation of the findings is subject to the data's limitations and the assumptions made during the analysis.