Laptop Price Predictor

The "Laptop Price Predictor" project, explores the use of machine learning techniques to predict laptop prices based on various features such as brand, RAM, storage type, and screen size. It encompasses data preprocessing, Exploratory Data Analysis (EDA), and visualizations. Statistical analyses are used to gain insights into how these features impact prices.

The project moves on to feature engineering to create new features and select the most relevant ones for the model. Various regression models, including Linear Regression, Ridge Regression, and Random Forest, are built and evaluated using metrics such as Mean Absolute Error (MAE) and R-squared (R²). Hyperparameter tuning is performed to optimize the model's performance.