In a world increasingly driven by complex machine learning models, the simplest solutions can often be the most effective in solving real-world challenges. In this talk, I will share the journey of developing a pressure progression prediction model in Python to forecast the ripeness of avocados. This model helps to optimize the supply chain and contributes to sustainability by reducing food waste. Using Python libraries such as pandas, numpy, statsmodels, and plotly, the model is continuously refined to improve accuracy and provide actionable insights.

The avocado and fruit industry in the Netherlands, like many other markets, faces the challenge of managing perishable goods. The constant pressure to deliver fresh, high-quality products without waste makes predictive modeling a powerful tool for optimizing inventory and reducing losses. Ripewise (https://ripewise.com/), a product of Experience Fruit Quality (https://experiencefruitquality.nl/), leverages this approach to predict avocado ripeness, enhancing inventory management and offering a more sustainable solution to an industry-wide problem.

After attending this talk, the audience will learn how to apply machine learning techniques using simple, effective Python tools. Whether someone is a beginner or have some experience with Python, they will gain valuable insights into data preprocessing, model development, and result interpretation. Most importantly, the audience will adopt a mindset for creating practical predictive models, understanding that sometimes a simpler model is the best fit. By the end of the session, the audience will be equipped to tackle similar challenges with greater confidence and efficiency.