Finish basic data cleaning Try to make a benchmark using kNN

convert all training data in one file

• Data Cleaning Code is dcp.py
• Beside weather_data,traffic_data & time, I have included previous 3 gaps of each data points as their features as well. Just to remind you.
• didi_concat_data.ipynb is used to join training data different day to a single csv file.
• My knn code is in didi_knn_on_training_set.ipynb

The final MAE result on Training set is 1.538

To run didi_knn_on_training_set.ipynb, you only need to unzip TrainingData.zip

「以上」

knn - benchmark on test-set-1 has been uploaded to UdaGroup

final MAE score = 9.6

• rewrite dcp.py into a better looking form
• add weekdays/weekend, price as new features
• implement feature selection
• Try better regression algorithms
• Find a way to include POI as features

「以上」

add weekdays/weekend, price as new features implement feature selection

the most useful features are three previous gap and two previous count

mae reduced to 5.3

data cleaning finished, csv files have been uploaded to Udacity slack channel