YelpRatePrediction

How to use:

1, clone this repo and put the dataset in it, the organization should look like below:

YelpRatePrediction
├── yelp_academic_dataset_review.json
├── yelp_academic_dataset_business.json
├── yelp_academic_dataset_tip.json
├── yelp_academic_dataset_checkin.json
├── yelp_academic_dataset_user.json
├── code
|   ├── data_to_vector.py
|   ├── get_user_model.py
|   ├── extract.py
|   ├── linear_regression.py
|   ├── splitDataset.py

2, run the files in the following order:

2.1: run splitDataset.py, this file split the whole review dataset into 3 parts
You should get data_modeling.json, data_training.json and data_testing.json
2.2 run extract.py, this file encode each item in the business dataset into a simpler representation
You should get restaurants_encoded.json
2.3 run user_model.py, this file gets you the user model
You should get user_model.json (note: if your computer does not have enough memory to run this, download the same file on the shared google folder and put it into the code foler)
2.4 NOTICE!! use the file data_to_vector.py as follows: 2.4.1: run it directly, you should get testing_X.json and testing_Y.json
2.4.2: modify the line 4,7,8 (no need to care about line 5), change the testing substring in them into training, then you shuold get training_X.json and training_Y.json
. the 4 result files are training and testing data encoded into vectors.
2.5 run linear_regression.py, the final result should be printed.