The goal of this project is to build a robust machine learning model that predicts laptop prices accurately. As the laptop market continues to expand with various brands and specifications, having a precise pricing model becomes crucial for both consumers and manufacturers. The project also has one user friendly interface that helps the user to enter their desired configurations and predict the price of their dream laptops.
This is a end-to-end Machine Learning Project that uses a Supervised Learning Regression Model which has been trained upon laptop dataset using a series of regression algorithms. The project EDA and Model Training part is conducted using Jupyter Notebook and the Application is generated upon Visual Studio Code. Libraries like Sklearn and Streamlit have been used in the process. The web-application is being hosted upon Streamlit Share.
The underlying dataset consist around 1303 records and 13 features. Among the features are the likes of Company, Type, Inches, ScreenResolution, Cpu, Ram, Memory, Gpu, OpSys, Weight, Price and few unnecessary features. The dataset is very raw and requires alot of cleaning and preprocessing.
- Import the dataset and explore its shape, sample, and presence of NULL values, duplicate records, missing values, rubbish values.
- Extract relevant features from the dataset and changing datatypes of features.
- Transform and create new features to enhance model performance.
- Visualizing the data and the patterns that it create by performing Univariate, Bivariate Analysis using various various types of charts.
- Selecting the appropriate features that affect the price of the laptop by checking their correlation with the dependent variable.
- Training different regression algorithms like (linear regression, decision trees, gradient boost etc.).
- Evaluate model performance using metrics like (R2_Score, Mean Absolute Error, Mean Squared Error ).
- Selecting the best model.
- Develop a web application using Streamlit where users can input laptop configurations and predict prices for their configuration.
- Deploy the trained model and its integrated api on Streamlit Share.
- By creating an end-to-end machine learning solution, we empower users to make informed decisions when buying or selling laptops. Whether you’re a data science enthusiast or a laptop shopper, this project provides valuable insights into laptop pricing trends. 😊
- Feel free to explore the code and dive deeper into the world of laptop price prediction! 🚀