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

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

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

Tools