Laptop-Price-Dataset

Data Collection

1. Collected laptop specifications and price data from multiple sources (e.g., websites, existing datasets).
2. Features collected include:
   • Processor (categorical)
   • Processor speed (numeric)
   • RAM (numeric)
   • Storage (numeric)
   • Graphics card (categorical)
   • Operating system (categorical)
3. Target variable collected: price (numeric).

Data Preprocessing

1. Clean and handle missing values.
2. Encode categorical variables (e.g., processor, graphics card, operating system).
3. Scale/normalize numeric features (e.g., processor speed, RAM, storage).
4. Split data into training set (80%) and test set (20%).

Model Training

1. Train a Random Forest regression model on the training data.
2. Hyperparameter tuning:
   • Number of estimators
   • Maximum depth
   • Minimum samples split
   • Minimum samples leaf
3. Evaluate model performance on the test data using:
   • Mean Absolute Error (MAE)
   • Mean Squared Error (MSE)
   • R‑squared

Model Deployment

1. Create a RESTful API allowing users to input laptop specifications and receive a predicted price.
2. Build a web application to visualize model predictions and enable user interaction.