WGU C964 Capstone Task 2
Camden Bodden (Student ID: 011056755)
2/28/2025
Computer Science Capstone
Western Governors University
The Binder Badge below allows you to automatically launch the development environment. When clicked, it will download the required dependencies and open a web-based version of the Jupyter Notebook
📌 Project Overview This project is a Machine Learning Regression Model designed to predict laptop prices based on various hardware specifications. It is developed as part of the WGU C964 Computer Science Capstone and runs on My Binder using a Jupyter notebook, allowing users to interact with an intuitive user interface (UI) to estimate laptop prices.
🛠 Key Features
Supervised ML Model: Uses Linear Regression to predict laptop prices.
Feature Engineering: Converts categorical laptop specs (brands, CPU, GPU, OS, etc.) into one-hot encoded features.
Interactive UI: Built with ipywidgets, enabling users to input laptop specifications and get instant predictions.
My Binder Compatibility: Designed to run without installations—simply open the binder link and start predicting!
Data Visualization: Includes scatter plots to compare actual vs. predicted prices for model evaluation.
🚀 How to Use
Open the My Binder notebook (link provided). Select restart and run all cells. Adjust the laptop specifications using sliders and dropdown menus. Click the "Predict Price" button to generate an estimated price. View results instantly within the notebook!
📂 Files in This Repository WGU_C964_Capstone.ipynb → The main notebook with model training, UI, and predictions. laptop_price_data.csv (if applicable) → The dataset used for training the model.