Laptop Price Prediction

This project aims to predict the price of a laptop based on various features. It utilizes machine learning techniques to train a model and make predictions.

Project Structure

The project has the following main files and folders:

• Data: This directory contains the dataset used for training the model.
• config: This directory contains various YAML files for configuration purposes.
• src: This directory contains the main source code of the application.
  • Components: This directory contains four main components of the application:
    • data_ingestion.py: Handles the ingestion of data from the dataset.
    • data_transformation.py: Performs necessary data transformations and feature engineering.
    • data_validation.py: Validates the input data for prediction.
    • model_trainer.py: Trains the machine learning model based on the transformed data.
  • Config:
    • configuration.py: Sets up the various file paths which will be needed in the components folder
  • Entity: This directory contains two entity files:
    • model_factory.py: Responsible for training the model and checking which one performs the best.
    • prediction.py: Responsible for performing the prediction on the data coming in from the UI
• static: Contains CSS files
• templates: Contains HTML files
• app.py: The main script to run the web application for predicting laptop prices.
• pipeline.py: Script to be run first, responsible for training the machine learning model.
• requirements.txt: Contains a list of required Python packages for running the project.

Getting Started

To run this code locally, follow these steps:

1. Make sure you have Python installed (version 3.0.0 or higher).
2. Install the required packages by running the command: pip install -r requirements.txt.
3. Update the model.yaml file in the config folder if you want to try different models.
4. Run the pipeline.py script to train the machine learning model: python pipeline.py.
5. Once the model is trained, run the app.py script to start the web application: python app.py.
6. Access the web application through your browser at http://localhost:5000.