A look at the project ahead

In this project, you’ll learn how to apply Multiple Linear Regression to analyze past sales data and, more importantly, explore how to interpret the model’s results using explainable AI techniques. You’ll explore traditional methods such as Feature Importance and Effect plots to visualize the impact of each feature on sales, while leveraging IBM Granite models for a more advanced analysis. Generative AI (LLMs) will also be used to automate the explanation process, transforming raw model outputs into easy-to-understand reports. This hands-on experience will prepare you to analyze both the data and the explanations behind model decisions—a skill increasingly required in fields where transparency is vital.

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What you’ll learn

1. Build and interpret a Multiple Linear Regression model

   Learn how to construct a Multiple Linear Regression model using Scikit-learn to predict bike rentals based on various factors such as weather, seasonality, and holidays. You’ll also explore how to interpret the model’s coefficients to understand the contribution of each feature to the predictions.

2. Visualize feature importance with Weight and Effect plots

   You’ll discover how to use Weight plots and Effect plots to visualize which features have the greatest impact on bike rental predictions. These tools will help you identify key drivers of demand, such as temperature, windspeed, and weather conditions, offering actionable insights for decision-making.

3. Automate explanations with Generative AI

   Leverage Generative AI techniques, particularly Large Language Models (LLMs), to automatically generate natural language explanations of the model’s predictions. This allows you to transform raw machine learning outputs into easy-to-understand reports, making the results more accessible to business users and stakeholders.

4. Apply Explainable AI (XAI) for model transparency

   Master Explainable AI techniques to ensure transparency in your model’s predictions. You’ll learn how to break down complex models into interpretable components, ensuring trust and accountability in AI-driven decisions.

5. Use Scikit-learn for feature standardization

   You’ll learn how to preprocess your data using Scikit-learn’s tools for standardizing features, improving model accuracy and ensuring your inputs are consistent for linear regression.

Why are these skills essential for AI-driven decision making?

Who should complete this project?

1. AI and Machine Learning enthusiasts: Anyone looking to understand how to build interpretable models using Scikit-learn, XAI techniques and IBM Granite models, while also incorporating Generative AI to enhance model explanations.
2. Data scientists and Analysts: Professionals who need to automate decision-making in businesses driven by external factors (e.g., bike rentals, retail), and want to ensure their models are both accurate and explainable.
3. Developers of AI-powered applications: Engineers building applications that rely on machine learning predictions and want to provide transparency to users through automated, natural language explanations.
4. Bike rental business owners and operations managers: Individuals responsible for optimizing day-to-day operations, such as determining bike inventory and staffing needs, based on data-driven predictions. By understanding and leveraging AI models, they can better align resources with customer demand and improve efficiency.

What you’ll need

• Basic Python programming knowledge: Familiarity with Python will help you navigate through the machine learning workflows, including model building, feature engineering, and data visualization.
• Understanding of Scikit-learn: A basic grasp of Scikit-learn and its linear regression functionalities will be useful for building predictive models and interpreting the results.
• Familiarity with AI concepts: Understanding AI concepts such as Explainable AI, Generative AI, and machine learning will help you make the most of this project’s hands-on learning experience.
• A current version of a web browser: To run the project and test the chatbot interface, you’ll need a web browser such as Chrome, Edge, Firefox, or Safari.