💻 Laptop Price Predictor

A machine learning project that predicts laptop prices based on their specifications using various regression models. This end-to-end pipeline includes data preprocessing, feature engineering, model training, evaluation, and visualization.

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📊 Project Overview

This project explores a dataset of laptops and their specifications to predict prices using multiple regression techniques. The best-performing model is deployed through a pipeline that includes preprocessing and prediction steps.

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🔧 Technologies Used

• Python 🐍
• Pandas
• NumPy
• Matplotlib & Seaborn
• Scikit-learn
• XGBoost

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📁 Dataset

The dataset laptop_data.csv includes the following features:

• Company (Brand)
• Type (Notebook, Ultrabook, etc.)
• RAM, Weight, Screen Size
• Processor, GPU, Operating System
• Memory (HDD, SSD, etc.)
• Target Variable: Price

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🧪 Models Implemented

• Linear Regression
• Ridge & Lasso Regression
• Decision Tree Regressor
• K-Nearest Neighbors Regressor
• Random Forest Regressor ✅ (Best Performance)
• Gradient Boosting, AdaBoost, Extra Trees
• XGBoost
• Support Vector Regressor (SVR)

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🏆 Best Model Performance

Model: Random Forest Regressor
Hyperparameters:

• n_estimators = 100
• max_depth = 15
• max_samples = 0.5
• max_features = 0.75

Evaluation Metrics:

Metric	Value
R² Score	0.88
MAE	0.15 (after scaling)