This is an assignment for my Data Mining course.
Description
Comparing the performances of multi-layer perceptron, k-nearest neighbors, random forest, gradient boosting and extreme gradient boosting regressions on laptop data to predict the price.
Libraries
pandas, numpy, seaborn, matplotlib.pyplot, sklearn
- Missing value analysis
- Data transformation
- Model evaluation
- Feature importance
- Hyperparameter tuning with Grid Search Cross-Validation
Data Set
The data set is publicly available on Kaggle.