At a Glance

Name: Optimizing Business with IBM Granite 3.0 and Explainable AI
Price: Free CAD
Rating: 4.9 (11 reviews)
Author: joseph_santarcangelo, hailey_quach, sina_nazeri, fateme_akbari

Streamline business decisions with Explainable AI (XAI), Gen AI, and IBM Granite 3.0 models. Apply these to a bike rental business by leveraging interpretable linear regression models to predict demand, staffing, and inventory needs based on weather and time of year.

In the era of AI and machine learning, it’s essential to not only analyze sales data accurately but also understand how those results are generated. Explainable AI (XAI) is key to gaining insights into the decision-making process of models, building trust, and ensuring accountability. This project focuses on analyzing past sales data using Multiple Linear Regression, an inherently interpretable model, and introduces explainable AI techniques such as Feature Importance and Effect Plots to illustrate how different features influence sales. By incorporating IBM Granite models, you'll enhance the analysis while maintaining interpretability. Upon completing this project, you'll be equipped to analyze and communicate model results in a way that stakeholders can understand and act upon.

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.

What you’ll learn

By completing this project, you will gain valuable skills to:

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.
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.
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.
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.
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?

In the age of AI, making data-driven decisions is more crucial than ever, especially for businesses like bike rentals that depend on fluctuating external factors such as weather and seasonality. The ability to predict demand allows businesses to optimize resources, improve customer service, and reduce operational costs.

However, accurate predictions are only part of the equation. In fields such as retail, finance, and healthcare, model transparency is equally important. You need to not only predict outcomes but also explain why certain features (e.g., temperature, holiday status) influence those predictions. This is where Explainable AI (XAI) techniques come into play, allowing you to build trust and accountability in your machine learning models. Additionally, the use of Generative AI to automatically generate insights helps scale the explanation process, making it easier to communicate AI-driven decisions to non-technical stakeholders.

By the end of this project, you will not only develop strong skills in building and interpreting linear regression models, but also be equipped to use cutting-edge tools like IBM Granite 3.0 models, Generative AI and XAI to improve transparency and trust in AI systems.

Who should complete this project?

This project is perfect for:

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.
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.
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.
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

Before starting this project, ensure you have the following:

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.

Start this guided project today and learn how to build, interpret, and explain a Multiple Linear Regression model with IBM Granite to analyze past sales based on key features!

Offered By: IBMSkillsNetwork