Overview

Laptops Price Prediction Using Web Scraping and Machine Learning

Overview

This project is divided into five parts to demonstrate the process of collecting data from online stores, preprocessing it, building a machine learning model to predict prices, creating a graphical user interface (GUI) for user interaction, and deploying an API service.

Part 1: Data Collection and Model Building

• Collect data from online stores to train a machine learning model for price prediction.

Part 2: Data Preprocessing and Analysis

• Perform data preprocessing to clean missing and invalid data.
• Conduct analysis and visualization to gain insights into the dataset.

Part 3: Graphical User Interface (GUI)

• Develop a GUI to interact with users and gather required information for price prediction.
• Users are prompted to input CPU type, RAM size, GPU type, SSD and HDD size, screen size, and operating system.

Part 4: Machine Learning Model Implementation

• Build a machine learning model to predict prices based on the collected features.
• Display predicted prices for the specified features.

Part 5: API Service Deployment

• Deploy an API service to provide price prediction functionality to users.

Hint:

• Users must input all required features, but can select at least one type of hard drive.
• They can also choose to view all laptops within a certain price range.

Additional Features

• NLP Model Usage: Incorporate NLP models for text analysis and processing of product descriptions.
• Regular Expression for NLP: Utilize regular expressions for advanced text pattern matching and extraction.

Usage

• Clone the repository and navigate to the project directory.
• Install the required dependencies.
• Run the appropriate scripts for data collection, preprocessing, model training, GUI development, and API deployment.

This Project’s GitHub Repository