The product mix modeling code is a comprehensive script that employs various machine learning models to predict and analyze the sales performance of products in the context of Kellogg's cereals. The code utilizes a dataset, 'PosMacroSegmentTradeWalkSwitch.csv,' which combines diverse data sources such as Point of Sale (POS), macroeconomic indicators, segmentation information, trade data, walk data, and switching data. These datasets collectively provide a rich foundation for understanding and predicting product sales trends.

Feature Engineering:
The script begins with feature engineering, where a new feature, 'similar private label SKU,' is created. This feature identifies products with similar characteristics to private label SKUs within each cluster. This can be crucial for understanding and addressing competition dynamics within specific market segments.

Data Filtering and Transformation:
The dataset is then filtered to focus on ASDA Kellogg's products. Relevant features are selected for modeling, including product description, unit and value sales, pricing information, customer segments, trade investment metrics, and switching indices. Additionally, a new feature, 'Invesment/valueSales,' is created to understand the investment-to-sales ratio.

Temporal Analysis:
The code identifies product trends between 2021 and 2022 by calculating the total sales ('valueSales') for each product in these years. This temporal analysis provides insights into how products have performed over the specified period.

Discount Calculation:
Discount information is computed using the 5-week rolling percentile of unit prices. The discount is determined based on a threshold, and a new feature, 'discount,' is created to flag products with discounts. Non-discounted sales are also computed for further analysis.

Data Standardization and Factorization:
The script standardizes numerical features and factorizes categorical features, ensuring consistent scales for modeling. This step is crucial for training machine learning models effectively.

Model Training:
Four machine learning models are trained to predict product sales:

Support Vector Regression (SVR) - Inclusive of all features.
XGBoost Regression - Inclusive of all features.
SVR - Excluding 'unitSales' and 'valueSales/ACV.'
XGBoost Regression - Excluding 'unitSales' and 'valueSales/ACV.'
Model performances are evaluated using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAE%).

Ranking and Residual Analysis:
The script ranks products based on original and predicted sales using different models. Residual analysis is conducted to understand the wellness of fit for the models, visualized through histograms of residuals.

Saving Results:
The final results, including SKU rankings, feature importance scores, and product mix data, are saved to CSV files for further analysis and business insights.

Product Mix Analysis:
The script conducts a detailed analysis of the product mix. It calculates various metrics, such as trends, cumulative sales percentages, and the proportion of sales within each SubSegment. The standardized product mix is also created for better comparability and analysis.