This project aims to predict laptop prices using a machine learning model. The code starts by importing the necessary libraries, such as NumPy and pandas, and loading a dataset from a URL containing laptop specifications and prices. The dataset is then examined to understand its structure and content, confirming that there are no missing values.
Next, the code processes the data by cleaning and transforming specific columns. For example, the 'Weight' column values are converted from strings to numerical values, and the 'Ram' column values are stripped of the 'GB' text and converted to integers. Additional columns are created to indicate whether a laptop has a touchscreen or an IPS display.
The 'Company' and 'Cpu' columns are simplified by grouping less common entries into an 'Other' category. The data is further processed to extract key parts of the 'Cpu' and 'Gpu' information and categorize the operating systems into broader groups like Windows, Mac, Linux, and Others.
Finally, the dataset is prepared for machine learning by dropping unnecessary columns and creating dummy variables for the categorical features. This transformed dataset can now be used to train a machine learning model to predict the price of laptops based on their specifications.