Optimizing Sustainability: Predicting Food Waste with ActableAI and aNumak & Company

Introduction:

In today’s world, where sustainability and efficiency are paramount, the issue of food waste has emerged as a significant challenge. One of the primary sources of food waste is the restaurant industry, with only a small portion of excess food recycled or donated. Not only does this wastage harm the environment, but it also poses financial implications for businesses.

The advent of technology has opened doors to innovative solutions that address this problem head-on. The power of Machine Learning (ML) and Artificial Intelligence (AI) can be leveraged in the Actable AI platform to predict the amount of food waste and its contributing factors.

Understanding the Data

Before diving into how Actable AI and aNumak & Company ® Company optimize results and drive impact in predicting food waste, it’s crucial to understand the importance of data in this context. Accurate and comprehensive data is the foundation for effective predictive analytics.

Data collection for predicting food waste can come from various sources, such as restaurant orders, inventory records, weather forecasts, and historical trends. Once this data is collected, it can be uploaded to the @actable.ai platform (either via an Excel spreadsheet CSV file, connecting directly to a database, or using the Google Sheets add-on). The Actable AI platform provides robust tools to visualize and analyze this data, allowing for a deep understanding of the relationships between different variables.

Leveraging Predictive Analytics

The correlational analysis tool is one of the critical tools in the Actable AI platform that plays a pivotal role in tackling food waste. This tool measures the strength of relationships between variables, allowing businesses to identify patterns and factors contributing to food waste.

Settings for the correlational analysis:

• Correlation Target: Amount of food waste.
• Compared Factors: Variables to measure correlation with the target.
• Number of Factors to Display: Choose the number of factors to display.
• Show Values on Bar Chart: Decide whether to show values on the bar chart.

The results are generated and displayed after configuring these settings and clicking the ‘Run’ button. For instance, it may reveal that higher pricing leads to higher food waste, while moderate and low pricing reduces waste. Factors like the number of guests and the quantity of food also contribute to destruction.

Training a Machine Learning Model

Once a deeper understanding of the data is gained through correlation analysis, the next step is to train a machine learning model to predict food waste. This can be done through Actable AI’s ‘regression’ analytic, with options including:

• Predicted Target: Amount of food wastage.
• Predictors: Features used to indicate the target.
• Explain Predictions: Option to generate Shapley values for interpretability.

Advanced options, such as specifying models and hyperparameters, are also available. Actable AI leverages AutoML techniques to train and select the best-performing model.

Performance Metrics of the Best Model

After training, the model’s performance is evaluated using metrics such as Root Mean Squared Error, Mean Absolute Error, Median Absolute Error, and R2. These metrics indicate how well the model will likely perform on unseen data, with lower error values suggesting better performance.

Feature Importance of the Best Model

Understanding which features are deemed necessary by the model is crucial. In this context, pricing, the number of guests, and food quantity are significant factors affecting food waste. Conversely, event type and seasonality have little impact.

Predicted Values and Shapley Values

Comparing predicted values with ground-truth values and examining Shapley values provides insight into how each variable affects food waste predictions. It helps determine which variables can be adjusted to reduce food waste effectively.

PDP and ICE Plots

PDP and ICE plots offer a visual representation of how model predictions vary with different values of variables. For example, higher pricing and more guests increase food waste.

Leaderboard of Models

The ‘leaderboard’ tab provides metrics for all trained models, including training time, prediction time, hyperparameters, and variables used. This information aids in selecting the most suitable model for specific applications.

Live Model and Counterfactual Analysis

The trained model can be used with new data through the ‘Live Model’ tab, where predictions can be generated interactively. Additionally, counterfactual analysis helps determine the effect of changing variables on food waste predictions.

aNumak & Company ® Consulting with Large Enterprises

aNumak & Company ®, in collaboration with Actable AI , brings consulting expertise. aNumak & Company ® specializes in working with large enterprises to optimize their operations and sustainability efforts. They help businesses gather and prepare their data for analysis, ensuring that the correct data is collected from various sources, including point-of-sale systems, inventory management, etc.

aNumak & Company ® works with Actable AI, leveraging their predictive analytics platform to provide large enterprises with data-driven insights and solutions to address food waste challenges. Their consulting services enable enterprises to make informed decisions and take actionable steps to minimize food waste, reduce costs, and improve sustainability.

Conclusion

The Actable AI platform, in collaboration with aNumak & Company ®, offers a comprehensive solution for analyzing data and generating predictive models to estimate food wastage. By harnessing the capabilities of machine learning, advanced analytics, and consulting expertise, businesses can gain insights into the factors contributing to food waste and take actionable steps to reduce it. This not only improves sustainability but also has a positive impact on the environment and financial efficiency.

For more details and hands-on examples, explore Actable AI’s resources, including videos, articles, and case studies, to learn how it can help drive impact across various industries, including E-Commerce and retail, Technology, Gaming, Healthcare, Financial Services, Insurance, Higher Education, and beyond.

Explore the accurate model @ActableAI & aNumak & Company ® built for predicting food wastage: Predict Food Waste Model.

By harnessing the capabilities of Actable AI and the consulting expertise of aNumak & Company ®, large enterprises can optimize their operations, reduce food waste, and contribute significantly to a more sustainable and efficient future.

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