The Objective of the study is to build a regression model to predict laptop prices based on the specifications of it. similar models can be built based on the specifications of other products as well. The main aim is to demonstrate the use of MLAutomata in building regression models without any code. MLAutomata is an autoML platform where one can build various machine learning problems without any code and also one can build models in a short amount of time. MLAutomata contains 6 modules

All these modules can be used in building different machine learning algorithms based on the business problem. However in this particular example we will see how build a regression model within the MLAutomata and also how to get Laptop price predictions and also explanations. The main objective of the study is to build a predictive model which can predict laptop prices as accurately as possible.

Predicting laptop prices accurately is crucial for consumers, as it helps them make informed purchasing decisions based on their budget constraints and desired features. For manufacturers, price prediction enables them to optimize pricing strategies, adjust inventory levels, and anticipate market demand more effectively.

The dataset sourced from Kaggle contains information about various laptop models, including features such as processor type, RAM size, hard disk size, screen size, GPU details, operating system, and price. The dataset provides a comprehensive set of attributes that can be used to build a predictive model for estimating laptop prices.

The dataset is divided into training and testing sets using a 80-20 split, ensuring that the model is trained on a sufficiently large portion of the data while retaining a separate set for evaluation.

In conclusion, we have successfully built a predictive model for estimating laptop prices based on their specifications. The model demonstrates promising accuracy and can provide valuable insights for both consumers and manufacturers in the laptop market.