/Wayfair-customer-data-analysis-competition

Partake in an online competition of the famous home decor giant that involved data analysis and application of Classification and Regression models.

Primary LanguageJupyter Notebook

Wayfair-customer-data-analysis-competition

Motivation

To partake in an online competition of the famous home decor giant that involved data analysis and application of Classification and Regression models.

Goals

  • To apply multiple Machine Learning models on real world data
  • Data Wrangling, Visualizations and hyperparameter tuning.
  • Presentation and analysis

Technical Achievements

  • Advanced pre-processing
  • Application of XGBoosting, LDA, QDA, SVM, Random forest, AdaBoosting, Ridge Regression and LASSO regression and preparing the data for each model.
  • Comparison of all the models after 10 fold cross validated hyperparameter tuning using various evaluation metrics.

Challenges

  • Deciding how to handle ordinal, categorical and missing data.
  • Model Selection

Future Considerations

  • Heavier Data Visualizations
  • More advanced Regression evaluation techniques