The code reached an AUC of 0.8850 on the public leaderboard on kaggle for the Driver Telematic Analysis .

The code is structured as follows:

1. Step_1_Create_DataFrame.py:

In this step, the entire kaggle data set is read into data frames and saved in HDF5 files. This step only has to be done once, and reduces the time required for Step 2 and 3.

2. Step_2_Extract_Features.py:

Here, features for each trip are extracted, put in a data frame and saved in HDF5 format.

3. Step_3_Classify.py:

In this step, a supervised learning approach (Random Forest Classifier) is used. For this, a classifier is trained for each driver as positive set and 200 random trips from other drivers are used as negative training set.