Link of the demonstration : https://share.vidyard.com/watch/R1M86VEKNkY2Ej4aUA2Aio?
The data scientists who created the 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 would like to analyze the parameters of the model to highlight features that are important to explain the behaviour of the users, and maybe discover a new lever for action to improve the newsletter's conversion rate. 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.
The project can be cut into four steps :
- Part 1 : make an EDA and the preprocessings and train a baseline model with the file data_train.csv
- Part 2 : improve your model's f1-score on your test set (you can try feature engineering, feature selection, regularization, non-linear models, hyperparameter optimization by grid search, etc...)
- Part 3 : Once you're satisfied with your model's score, you can use it to make some predictions with the file data_test.csv. You will have to dump the predictions into a .csv file that will be sent to Kaggle (actually, to your teacher/TA). You can make as many submissions as you want, feel free to try different models !
- Part 4 : Take some time to analyze your best model's parameters. Are there any lever for action that would help to improve the newsletter's conversion rate ? What recommendations would you make to the team ?
Walmart's marketing service has asked you to build a machine learning model able to estimate the weekly sales in their stores, with the best precision possible on the predictions made. Such a model would help them understand better how the sales are influenced by economic indicators, and might be used to plan future marketing campaigns.
The project can be divided into three steps:
- Part 1 : make an EDA and all the necessary preprocessings to prepare data for machine learning
- Part 2 : train a linear regression model (baseline)
- Part 3 : avoid overfitting by training a regularized regression model
One of the main pain point that Uber's team found is that sometimes drivers are not around when users need them. For example, a user might be in San Francisco's Financial District whereas Uber drivers are looking for customers in Castro.
(If you are not familiar with the bay area, check out Google Maps)
Eventhough both neighborhood are not that far away, users would still have to wait 10 to 15 minutes before being picked-up, which is too long. Uber's research shows that users accept to wait 5-7 minutes, otherwise they would cancel their ride.
Therefore, Uber's data team would like to work on a project where their app would recommend hot-zones in major cities to be in at any given time of day.
Uber already has data about pickups in major cities. Your objective is to create algorithms that will determine where are the hot-zones that drivers should be in. Therefore you will:
- Create an algorithm to find hot zones
- Visualize results on a nice dashboard