💻 Used Laptop Price Prediction (ElectronicsBazaar) - ML

📌 Project Overview

This project predicts the price of second-hand laptops using Machine Learning techniques.
The dataset was scraped from ElectronicsBazaar and processed for model training and evaluation.

The workflow includes:

• Web scraping using Selenium
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Model training and comparison
• Final price prediction using the best model

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📂 Project Structure

Used-Laptop-Price-Prediction-ElectronicsBazaar-ML/
│
├── data/
│   ├── raw_laptop_data.csv
│   ├── cleaned_laptop_data.csv
│   └── predicted_prices.csv
│
├── scraping/
│   └── scrape_laptop_data.py
│
├── preprocessing/
│   └── data_preprocessing.py
│
├── EDA.ipynb
├── ML_Model.ipynb
├── requirements.txt
└── README.md

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🔍 Exploratory Data Analysis

EDA includes:

• Dataset overview
• Missing value analysis
• Correlation heatmap
• Statistical summaries

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🤖 Machine Learning Models Used

The following regression models were trained and evaluated:

• Linear Regression
• Ridge Regression
• Lasso Regression
• Decision Tree Regressor
• Random Forest Regressor
• Support Vector Regressor
• XGBoost Regressor

The best performing model (based on MAE and R² score) was selected and saved using joblib.

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🛠 Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• XGBoost
• Seaborn
• Matplotlib
• Selenium

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📈 Output

The trained model predicts laptop prices and saves results in: data/predicted_prices.csv

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📌 Conclusion

This project demonstrates an end-to-end Machine Learning pipeline: Data Collection → Data Cleaning → EDA → Model Training → Prediction.