1. Data preprocessing and exploratory analysis

• deal with missing values and categorical variables (use one-hot encoding)
• check for correlated variables
• analyse distributions of attributes
• visualise the data using two or three principal components

2. Logistic Regression model

3. More advanced models

3.1 Random Forest

3.2. Support Vector Machine

3.3. Feed Forward Neural Network

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For each model:

• standarize the dataset
• use the same cross-validation scheme
• inspect feature importance
• evaluate model with logloss, ROC-AUC and F1 classification metrics

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Resources

• dataset
• paper
• Python Machine Learning by Sebastian Raschka
• interesting talks:
  • Machine Learning 101 by Kyle Kastner (+ GitHub repo)
  • Classification using Pandas and Scikit-Learn by Skipper Seabold (+ GitHub repo)
  • Machine Learning with Scikit-Learn by Jake VanderPlas (+ GitHub repo)
  • Neural Nets for Newbies by Melanie Warrick (+ GitHub repo)

Keywords:

• dimensionality reduction
• cross-validation
• supervised vs unsupervised learning
• regression vs classification
• confidence intervals and p-values

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Tools

• conda
• jupyter notebook
• pandas
• sklearn
• seaborn