Participate in a Kaggle-like machine learning competitionand submit the model's predictions to your teacher/TA to be evaluated in an independent way. The data scientists who created a newsletter would like to understand better the behaviour of the users visiting their website. They would like to know if it's possible to build a model that predicts if a given user will subscribe to the newsletter, by using just a few information about the user.
They designed a competition aiming at building a model that allows to predict the conversions (i.e. when a user will subscribe to the newsletter). To do so, they open-sourced a dataset containing some data about the traffic on their website. To assess the rankings of the different competing teams, they decided to use the f1-score.
- Make exploratory data analysis (EDA) and the preprocessings
- Train a baseline model with the file data_train.csv
- Feature engineering
- Model training
- Make predictions and dump them into a .csv file that will be sent to your teacher/TA for evaluation in an independent way.
- Some feature engineering would have helped to improve the model's performance.