This is a project that aims to predict the prices of laptops based on various factors such as brand, processor, storage, and graphics card. The project uses a machine learning algorithm to make predictions and is built using the Streamlit library in Python.
To run the project, the following packages are required:
streamlit pandas numpy sklearn
The data used in this project was obtained from a Kaggle dataset and contains information on over 1000 laptops from various brands and specifications. The data includes information on the following features:
Brand Processor Storage Graphics card Price
The data was preprocessed to handle missing values and convert categorical variables into numerical variables. A Random Forest Regressor model was trained on the data and was used to make predictions on the prices of laptops based on the input features.
The project can be deployed as a web application using Streamlit. The app allows the user to input the specifications of a laptop and predicts its price based on the machine learning model.
The model achieved a good accuracy in predicting the prices of laptops based on their specifications. The web application makes it easy for users to obtain price predictions and is a useful tool for anyone looking to buy a laptop.