The objective of this project is to create end to end machine learning model to predict prices of laptop based on given set of features. If a user who is not aware of price of laptops, but knows the features by selecting the desired features and configurations, we can predict the price using our website.
This is basically a regression problem.
- Data Preprocessing and cleaning
- Exploratory data analysis
- Feature Engineering
- Modeling
- Website/UI
- Deployment in Cloud platform (Herouku)
https://laptopprice.herokuapp.com/
XGboost,Voting Regressor,Catboost,Gradient boost,LightGBM, Stacking,Random Forest,Extra Tree,Support Vector Regressor,Linear Regression, Lasso Regression,Ridge Regression,Decision Trees,Ada boost,K-Nearest Neighbors.
From the obtained results of the above models, XGBoost Classifier has highest model performance of 89.8%. So the model is saved to the file 'XGBoostRegressor.pickle.dat'
