💻 Laptop Price Predictor

• A Machine Learning based web application that predicts laptop prices based on configuration details.

🚀 Project Overview

• This project uses Data Analysis, Feature Engineering and supervised Machine Learning techniques to predict laptop prices based on hardware specifications.

• The model is trained on a Laptop Price Dataset and deployed using Streamlit for real-time predictions.

🛠 Technologies Used

• Python

• Pandas

• NumPy

• Seaborn

• Matplotlib

• Scikit-learn

• XGBoost

• Streamlit

• Pickle

🤖 Machine Learning Models Used

• Linear Regression

• Ridge Regression

• Lasso Regression

• K-Nearest Neighbors (KNN)

• Decision Tree Regressor

• Random Forest Regressor

• Extra Trees Regressor

• AdaBoost Regressor

• Gradient Boosting Regressor

• XGBoost Regressor

• Voting Regressor

• Stacking Regressor

📊 Dataset Details

• Dataset: Laptop Price Dataset

• Source: Kaggle

• Target Variable: Price

• Problem Type: Regression

Features Included:

• Company

• TypeName

• RAM

• Weight

• Touchscreen

• IPS Display

• Screen Resolution

• CPU

• GPU

• HDD

• SSD

• Operating System

🔍 Methodology

1️⃣ Data Cleaning

• Removed duplicate rows

• Checked missing values

• Dropped unnecessary columns

• Converted RAM & Weight into numeric format

2️⃣ Feature Engineering

• Extracted Touchscreen and IPS features

• Calculated PPI (Pixels Per Inch)

• Simplified CPU brands (Intel i3/i5/i7, AMD, Others)

• Split Storage into HDD and SSD

• Categorized Operating Systems (Windows, Mac, Others)

• Applied log transformation on Price

3️⃣ Encoding & Pipeline

• Used ColumnTransformer

• Applied OneHotEncoder

• Built complete ML Pipeline using Scikit-learn

📈 Model Evaluation

Evaluation Metrics Used:

• R2 Score

• Mean Absolute Error (MAE)

🏆 Best Performing Models

• Random Forest

• Extra Trees

• Gradient Boosting

• Stacking Regressor

✅ Ensemble models performed better than simple linear models.

💡 Key Insights

• Price strongly depends on RAM, SSD, CPU, GPU, and PPI

• Log transformation improved performance

• Feature engineering significantly boosted accuracy

• Ensemble techniques provided the best results

🌐 Deployment

• Final model saved using Pickle

• Integrated into a Streamlit web app

Users Can:

• Select laptop brand

• Choose specifications

• Click Predict Price

• Get instant predicted price

▶️ How to Run the Project

• #Install dependencies

• pip install -r requirements.txt

• #Run the Streamlit app

• streamlit run app.py

🔮 Future Improvements

• Hyperparameter tuning using GridSearchCV

• Add Cross-Validation

• Deploy on Streamlit Cloud

• Add visualization dashboard

• Add feature importance graph

• Try Deep Learning models