Laptop Price Prediction - End to End Machine Learning project

Welcome to the Laptop Price Prediction project! This machine learning model is designed to predict laptop prices based on various specifications.

Features Overview

• Brand: The brand of the laptop.
• Spec Rating: A numerical rating representing the laptop's specifications.
• RAM: Random Access Memory size.
• ROM: Storage capacity.
• ROM Type: Type of storage (e.g., SSD, HDD).
• Display Size: Size of the laptop display.
• Resolution Width/Height: Screen resolution details.
• OS: Operating System.
• Warranty: Warranty period in months.
• GPU Type: Graphics Processing Unit.
• CPU Core/Threads: Number of CPU cores and threads.
• Processor Brand/Gen/Version: Details about the laptop's processor.

Prediction Target

• Price: The predicted price of the laptop.

🛠 Skills

Python, Machine learning, MLflow, AWS, Github Actions, Flask.

How Project Works

The model takes in these laptop specifications and crunches the numbers to provide an estimated price. The project is structured with components such as data ingestion, data validation, data transformation, model training, and model evaluation. With Flask, the model is deployed, allowing users to interact with it through a user-friendly interface.

Project Screenshots

Get Started

Explore the src directory to dive into the main package files and check out the various components. If you're interested in the nitty-gritty details, take a look at the data pipelines, entities, and configuration classes.

Documentation

Run Locally

Clone the project

  git clone https://github.com/kharramahendra/Laptop-Price---End-to-End-ML-Project.git

Go to the project directory

Install dependencies

  pip install -r requirements.txt

Start the server

  python app.py

🚀 About Me

I'm a passionate learner with a strong interest in data science. I'm currently focused on creating real-world projects using LLMs, transformers, and advanced machine learning techniques. I also have experience in web development, using Next.js, Django, and React to build data-driven web applications.

🔗 Links

Live Project