A laptop price predictor using machine learning is a system that utilizes machine earning algorithms to analyze historical data on laptop prices and predict future rices based on that data. The system may be trained on a large dataset of past aptop sales, which includes information such as brand, model, features, and sale rices. The machine learning algorithm then uses this data to identify patterns and trends in laptop pricing, which can be used to make predictions about future prices.
DATA COLLECTION: Most of the columns in a dataset are noisy and contain lots of information. But with feature engineering you do, you will get more good results. The only problem is we are having less data but we will obtain a good accuracy over it. The only good thing is it is better to have a large data. we will develop a website that could predict a tentative price of a laptop based on user configuration. The data is collected from Kaggle.com. But this dataset contains many columns hich are not need to add in project, so these columns removed from the dataset.
DATA PREPROCESSING: Data Pre-processing means Cleaning and preparing the data for use in the model. In this step the data is being filtered out and data conversion, data structuring is done in this process.
MODEL SELECTION: Model selection is the process of choosing the most appropriate machine learning algorithm for a given problem. This decision is based on the characteristics of the data and the specific requirements of the problem. Choosing the most appropriate machine learning algorithm for the task.
MODEL EVALUATION: Evaluating the performance of the model using metrics such as mean absolute error and R-squared.
DEPLOYMENT: Developing a web or mobile application for the system and deploying it for consumer and retailer use.
Python Machine Learning SmartSpecs