• Designed a web app that predicts the price of the laptop given the configurations.
• Developed Linear, Lasso, and Random Forest Regressors KNN, Decision Tree Regressor and Gradient Boost Regressor to get the best model.
• Deployed the Machine Learning model using streamlit library in Heroku

• Company (Laptop Company)
• TypeName (for ex.Notebook)
• Inches (size in inches)
• ScreenResolution (for ex. 1366x768)
• Cpu (for ex. Intel Core i5 7200U 2.5GHz)
• Ram(Random Access Memory in MegaBytes)
• Memory (Internal Storage HDD or SDD in Gigabytes)
• Gpu (for ex. Intel HD Graphics 520)
• OpSys (Operating System)
• Weight (Weight of the laptop)
• Price (price of laptop)

Used scikit-learn library for the Machine Learning tasks. Applied label encoding and converted the categorical variables into numerical ones.Then I splited the data into training and test sets with a test size of 20%. I tried three different models ( Linear Regression, Random Forest Regression, XGBoost) and evaluated them using Mean Absolute Error.

Deployed the model using Streamlit library on Heroku which is a Platform As A Service(PAAS)