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Data science case study: Click Rate Prediction using real-world Trivago data.

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Trivago Case Study

Data science case study targeting hotel entry click rate, based on real-world Trivago data. The report includes an in-depth description of my work and thought process. This project has been carried out in one week (time allotted for the challenge).

Prerequisites

I used Conda 4.5.11 with Python 3.6.5 on a machine implementing Windows 10 64-bit to set up my PyData stack. I worked within a new environment defined and activated via terminal as in the following:
> conda create --name trivago python=3.6.5 numpy pandas matplotlib seaborn scikit-learn py-xgboost
> conda activate trivago

The "main.py" script executes the whole workflow and saves the output in a logfile.
> python main.py

Highlights

  • EDA: characterized unknown feature by means of violin plots.
  • EDA: leveraged log-discretized pairplots with multiple KDE for regression analysis.
  • My pipeline involved stratified splitting, one-hot encoding, normalization, grid/randomized search, linear and polynomial regression with shrinkage, SVR, random forest and XGBoost with early stopping.
  • Significantly improved results by applying weighted oversampling strategy.
  • My best model achieved a weighted MSE of 0.91 and performed statistically significantly better than the naïve baseline on the test set (2.25).

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Data science case study: Click Rate Prediction using real-world Trivago data.

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