djauhar-36/Price-of-laptops-Algerian-mode

Machine Learning notebook for laptop price prediction using Google Colab and Scikit-learn. 💻 Laptop Price Prediction - Algerian Market A Machine Learning project developed entirely using Google Colab to estimate the prices of laptops available in the Algerian market. The dataset mainly focuses on laptops priced up to approximately 120,000 DZD (12 million centimes) . 📖 Overview This project uses several Machine Learning regression algorithms to predict laptop prices based on their hardware specifications. The models are trained and evaluated using the same dataset, then compared using the Root Mean Squared Error (RMSE) metric to automatically select the best-performing model. The final model predicts the estimated market price of a laptop from its encoded hardware specifications. 🚀 Technologies Google Colab Python 3 Pandas NumPy Scikit-learn Matplotlib 🤖 Regression Models K-Nearest Neighbors (KNN) Linear Regression Ridge Regression Lasso Regression Decision Tree Regressor Random Forest Regressor Gradient Boosting Regressor 📊 Model Performance (RMSE) Algorithm Parameters RMSE (DA) KNN k=3, metric='manhattan' 4247.97 Linear Regression fit_intercept=True 494.71 Decision Tree max_depth=8 4601.72 Random Forest 800 Trees 5098.88 Gradient Boosting 300 Estimators 2096.82 Ridge Regression alpha=1 8548.40 Lasso Regression ⭐ alpha=0.01 487.47 ✅ Lasso Regression achieved the best performance and was selected as the final prediction model. 📑 Dataset Encoding Reference All categorical features are encoded using numeric values to simplify preprocessing and improve model performance. The following tables explain the encoding used throughout the dataset. Brand (model) Code Brand 1 Dell 2 HP 3 Lenovo 4 Asus Processor (CPU) Code Processor 2 Intel Core i3 4 Intel Core i5 6 Intel Core i7 Storage Type (Type_Rom) Code Type 0 HDD 1 SSD Touch Screen (Tactile) Code Value 0 No 1 Yes Other Features Feature Description generation CPU generation (e.g. 8 = 8th Generatio …