The training data is pulled from raw logs. It has three columns that record user activity over time:
user_id: A hash that uniquely identifies the user.
activity_date: The date of the activity
activity_type: The type of activity

6sense would like to predict which users will purchase given the user's activities.

Using the training data, build a model that predicts which user_id's will make a purchase in the future. Use the model to score the test data from most likely to least likely to purchase.

Please submit the following:
(1) Descibe which activity types you believe are most useful in predicting which user will purchase in the future.
(2) Provide a file containing the 1000 user_id's most likely to convert.
(3) [optional] Any work used to generate the list and/or notes you'd like to include.

The dataset is available for download online on kaggle.

Question

The training data is pulled from raw logs. It has three columns that record user activity over time:
user_id: A hash that uniquely identifies the user.
activity_date: The date of the activity
activity_type: The type of activity

6sense would like to predict which users will purchase given the user's activities. 

Using the training data, build a model that predicts which user_id's will make a purchase in the future. Use the model to score the test data from most likely to least likely to purchase.

Please submit the following:
(1) Descibe which activity types you believe are most useful in predicting which user will purchase in the future.
(2) Provide a file containing the 1000 user_id's most likely to convert.
(3) [optional] Any work used to generate the list and/or notes you'd like to include.  

The dataset is available for download online on kaggle.

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