This project provides a comprehensive analysis of brand and market performance, enabling businesses to make data-driven decisions. It uncovers patterns, correlations, and benchmarks in the retail landscape using various models and visualizations.

• Brand Performance: Analysis of total and average sales volume and net sales per brand.
• Supermarket Performance: Evaluation of total and average sales volume and net sales per supermarket.
• Other Stores Performance: Assessment of total and average sales volume and net sales per client type.
• Discount Impact: Study of the correlation between discounts and sales volume across brands and supermarkets.
• Cost Efficiency: Examination of the ratio of total costs (COGS, distribution, and warehousing) to net sales per brand and supermarket.
• Product Preference: Identification of the most and least popular product size or pack per brand and supermarket.

• Brand Ranking
• Influence of Supermarkets
• Analysis of Discounts
• Operations Cost-Effectiveness
• Preferences for Product Size

• Brand Performance: Graphical depiction of brand-specific metrics.
• Client Performance: Visualization of different client types' performance.
• Discount Impact: Illustration of the correlation between discounts and sales volume.
• Cost Efficiency: Representation of brands and stores' cost efficiency.

The project follows a structured approach:

1. Data Preparation: Dataset cleaning and organization.
2. Exploratory Data Analysis (EDA): Initial data insights.
3. Visualization Creation: Development of graphical representations.
4. Models Implementation: Deployment of regression, time series, clustering, and classification models.
5. Documentation: Detailed project overview, metrics, visualizations, model details, and library dependencies.

• Linear Regression: To understand the relationship between discounts, costs, and sales.
• Time Series Analysis: To identify seasonal patterns or trends in sales.
• Clustering: To group sales based on cost efficiency.
• Decision Tree Regression: To predict net sales for each brand.
• Random Forest Regressor: To predict volume for each combination of brand and client type in the test set.

This project is beneficial for retail businesses seeking to optimize brand and supermarket performance. Data analysts and data scientists can utilize the models and visualizations for deriving actionable insights.

Ensure the following libraries are installed:

pip install pandas numpy matplotlib seaborn scikit-learn ipywidgets statsmodels numpy-financial

This in-depth analysis offers actionable insights for businesses, guiding strategies related to pricing, inventory management, and targeted marketing campaigns in the retail landscape.

We extend our gratitude to the contributors and the open-source community for their invaluable input.