Laptop Cost Evaluation Project

Live Demo → https://laptop-cost-evaluation-project.onrender.com/

A fast, user-friendly web app that instantly predicts a laptop’s market price from key specs like brand, CPU, RAM, storage, and more.

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📑 Table of Contents

• 🚀 Key Features
• 🗂 Project Structure
• ⚙️ Installation & Setup
• 🏃 Usage
• 📊 Data & Analysis
• 📋 Requirements.txt
• ☁️ Deployment
• 🔮 Future Work & Ideas
• 🤝 Acknowledgments

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🚀 Key Features

• Instant market cost estimation via a clean, single-page form
• Supports 16 categorical inputs + hidden defaults for ratings & reviews
• CPU-only TensorFlow backend for lightweight inference
• Live INR ↔ USD conversion on the client side
• Portable: runs locally (Windows/WSL/macOS) or on Render with zero-config

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🗂 Project Structure

.
├── app.py
├── laptop_data.csv
├── Laptop_Regression.ipynb
├── README.md
├── requirements.txt
├── model
│   ├── laptop_cost_model.h5
│   ├── meta.json
│   └── preprocessor.joblib
└── static
    ├── css
    │   └── style.css
    ├── icon
    │   └── laptop_icon.png
    ├── img
    │   └── bg.jpg
    └── js
        └── predict.js

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⚙️ Installation & Setup

Download and Install python 3.10.11 from this link and Add the path: C:\Users\user(name)\AppData\Local\Programs\Python\Python310\python.exe to Environment Variable (PATH).

🟦 PowerShell

py -3.10 -m venv tempenv; .\tempenv\Scripts\Activate.ps1

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🟠 Git Bash (or any Unix-style shell on Windows)

python3.10 -m venv tempenv && source tempenv/bin/activate

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⚫ CMD (Command Prompt)

py -3.10 -m venv tempenv && .\tempenv\Scripts\activate.bat

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🏃 Usage

1. Activate your virtual environment
2. Install dependencies:

   pip install -r requirements.txt

3. Run the app:

   python app.py

4. Open your browser at http://localhost:5000

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📊 Data & Analysis

• laptop_data.csv
  Raw dataset of about 1k rows of laptops with specs & prices.
• Laptop_Regression.ipynb
  Exploratory Data Analysis, feature engineering, model training & evaluation.

Feel free to explore or extend the notebook with new algorithms.

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📋 Requirements.txt

# Required for webapp to run
setuptools>=65.0.0
wheel>=0.40.0
Flask==3.1.1
joblib==1.5.1
numpy==2.1.3
pandas==2.3.0
scikit-learn==1.7.0
tensorflow-cpu==2.19.0

# Optional if you want to experiment with Laptop_Regression.ipynb
# lightgbm==4.1.0
# xgboost==1.7.6
# catboost==1.2
# matplotlib==3.7.2
# seaborn==0.12.2

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☁️ Deployment

This project is hosted on Render, with following adjustments:

• Set PYTHON_VERSION=3.10.11 in Render’s Environment tab
• Bind to the PORT env var in app.py (fallback to 5000 locally)
• Static assets served via {{ url_for('static', …) }} for correct routing
• PROJECT_DIR = os.path.dirname(os.path.abspath(__file__)) for relative path in app.py

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🔮 Future Work & Ideas

• Convert the Keras model to TensorFlow Lite for ultra-light inference
• Add real-time currency rates via a free API
• Build a comparison view: show competitor models & price deltas
• Expose a public REST API endpoint for batch predictions

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🤝 Acknowledgments

• Dataset & inspiration provided by Kaggle
• Free hosting and auto-deploy courtesy of Render
• Interactive development environment powered by Google Colab

Feel free to ⭐ the repo, file issues, or submit PRs for new features!