In my "laptop_price_predictor" project, I've explored several machine learning models including LinearRegression, DecisionTreeRegressor, KNeighborsRegressor, RandomForestRegressor, and GradientBoostingRegressor. Among these, RandomForestRegressor and GradientBoostingRegressor have delivered notably strong results. I've conducted thorough exploratory data analysis (EDA), gaining insights into the dataset's characteristics and relationships. Moving forward, my focus is on refining feature engineering techniques. Key areas for improvement include precise feature selection using correlation analysis and model-based importance metrics, robust handling of missing values, effective encoding of categorical variables, and exploring domain-specific features like processor specifications, RAM, storage type, and brand influence. By enhancing these aspects, I aim to optimize model performance and predictive accuracy for laptop pricing.