💻 Laptop Price Prediction

A Machine Learning project that predicts laptop prices based on specifications such as brand, RAM, storage, processor, GPU, and other hardware features.

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📌 Project Overview

This project builds a regression model that estimates laptop prices based on their hardware specifications. The project includes data analysis, preprocessing, model training, evaluation, and deployment using a web interface and API.

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🚀 Features

• Built a machine learning regression model to predict laptop prices from laptop specifications.

• Performed Exploratory Data Analysis (EDA) to understand price patterns and feature relationships.

• Applied data preprocessing techniques such as:

  • Missing value handling
  • Outlier treatment
  • Feature encoding
  • Feature scaling

• Trained multiple models including:

  • Linear Regression
  • Random Forest
  • Gradient Boosting

• Selected the best model using R² Score, MAE, and RMSE evaluation metrics.

• Developed an interactive Streamlit web application for easy price prediction.

• Built a FastAPI REST API to serve the trained model for backend inference.

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🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn
• Streamlit
• FastAPI

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📊 Model Evaluation

The models were evaluated using the following metrics:

• R² Score
• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)

The best-performing model was selected based on these metrics.

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📂 Project Structure

Laptop-Price-Prediction
│
├── data
│   └── laptop_data.csv
│
├── notebooks
│   └── EDA_and_model_training.ipynb
│
├── models
│   └── trained_model.pkl
│
├── app
│   ├── streamlit_app.py
│   └── api.py
│
├── requirements.txt
└── README.md

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🖥️ Streamlit Web Application

The project includes a Streamlit-based web interface where users can input laptop specifications and get a predicted price instantly.

(Add your Streamlit app screenshot here)

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🔗 FastAPI Model API

The trained model is exposed through a FastAPI REST API, allowing the model to be used in other applications.

Example endpoint:

POST /predict

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📌 Use Case

Users enter laptop specifications such as brand, RAM, storage, processor, and GPU, and the system predicts the estimated laptop price using the trained machine learning model.

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📈 Future Improvements

• Deploy the application on Cloud (AWS / GCP / Render / Hugging Face Spaces)
• Add more laptop features for better prediction
• Improve model performance with advanced ML algorithms

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⭐ If you like this project, feel free to star the repository!