Laptop Price Prediction & Market Analysis

A professional Machine Learning implementation designed to predict laptop market prices using high-dimensional hardware specifications.

Executive Summary

This project implements an end-to-end Machine Learning pipeline to analyze and predict laptop pricing. By leveraging advanced data cleaning and ensemble modeling, the system identifies the key hardware drivers that influence market valuation.

🛠️ Technical Stack

• Core: Python 3.x
• Data Engines: Pandas (Data Manipulation), NumPy (Numerical Computing)
• Machine Learning: Scikit-Learn (Linear Regression, Random Forest)
• Visualization: Matplotlib, Seaborn
• Preprocessing: Standard Scaling, One-Hot Encoding, Regex-based Feature Extraction

⚙️ Engineering Workflow

• Advanced Cleaning: Automated removal of non-numeric units and data type optimization.
• Feature Engineering: - Extracted Display Resolution (X and Y pixels) and Touchscreen capability using Regex.
  • Simplified complex CPU/GPU nomenclature into high-impact categorical features.
• Pipeline: Implemented a robust preprocessing pipeline to handle multi-collinearity and feature scaling.

📈 Model Performance & Benchmarking

The project benchmarked multiple algorithms to optimize predictive accuracy:

Model	MAE	RMSE
Linear Regression	12,434	18,152
Random Forest Regressor	10,671	17,658

Result: The Random Forest Regressor reduced the Mean Absolute Error (MAE) by 1,763 units, demonstrating superior handling of non-linear pricing trends and high-end hardware configurations.

📂 Dataset

The data used for this project can be found here: Dataset Link