This project focuses on predicting laptop prices using machine learning techniques. By analyzing various laptop features such as RAM, weight, display properties, and brand, we aim to build a predictive model that can estimate the price of a laptop. Project Overview The primary objective of this project is to develop a predictive model capable of estimating the price of a laptop given its unique set of features. Leveraging machine learning algorithms, we aim to empower users with a tool that simplifies the process of evaluating and understanding the pricing dynamics within the laptop market.
Our predictive model takes into account a wide range of laptop features, including but not limited to:
- RAM: The amount of memory available for processing tasks.
- Weight: The physical weight of the laptop, impacting portability.
- Processor: The type and performance of the central processing unit (CPU).
- Display Characteristics: Features such as touchscreen capability, IPS technology, and Pixels Per Inch (PPI).
- Storage: The presence and capacity of both Hard Disk Drives (HDD) and Solid State Drives (SSD).
- Graphics Card: The type and power of the laptop's graphics processing unit (GPU).
- Operating System: The software environment that facilitates user interaction.
Whether you're a tech enthusiast, a consumer researching a new laptop purchase, or a developer curious about machine learning applications, this project is designed for you. The Usage section in the README provides clear instructions on how to utilize the predictive model and make informed laptop price predictions.
To make navigation seamless, the project is organized into different sections:
- Data: Explore the dataset used for training and testing the model.
- Installation: Follow step-by-step instructions to set up the project locally.
- Model Training: Dive into the notebook explaining how the predictive model is trained.
- Evaluation: Understand how the model's performance is evaluated.
- Dependencies: Check out the list of libraries required to run the project.
- Contributing: Interested in contributing? Find guidelines in this section.
- License: Understand the terms under which this project is licensed.