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Laptop Price Estimator

I created a model to estimate laptop prices using different types of regression models:

  • Random Forest
  • Decision Trees
  • Gradient Boosting
  • XGBoost
  • AdaBoost
  • Linear Regression

Among these, the Random Forest Regression model performed the best, with an accuracy of 85%.

Data Source

I gathered the latest data from various online sources to ensure the model is up-to-date with current market specifications. Using the most recent data makes the model more reliable for predicting current laptop prices.

Outliers and Data Preprocessing

While exploring the data, I found some unusual outliers. I fixed these using the IQR method. However, removing all outliers wasn't feasible because some high-priced laptops are genuine. To handle this, I performed feature engineering to extract more features from existing ones, which helped improve the model's accuracy.

Model Deployment

This model helps you find the price of a laptop based on its specifications. You can try it out on my Hugging Face account:

Link: https://huggingface.co/spaces/Vishalpainjane/Laptop_Cost

Running Locally

To run this model on your local machine:

  1. Clone the repository:

    git clone https://github.com/VishalPainjane/Laptop_Cost.git

  2. Navigate to the interface folder:

    cd interface

  3. Run the interface.py file:

    python interface.py

And that's it! The program will run on your local machine.