# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
df = pd.read_csv('../input/data-exploration/avgratings.csv')
df.count()
"""
For this part, I'll be using Surprise SVD
"""
from surprise import Reader, Dataset, SVD
"""
Start reading in ratings data
"""
reader = Reader()
data = Dataset.load_from_df(df[['user_id', 'venue_id', 'rating']], reader)

"""
Create SVD object and start training!
"""
svd = SVD()
trainset = data.build_full_trainset()
svd.fit(trainset)
"""
Save model to file
"""
from surprise import dump
file_name = 'movie_svd_model'
dump.dump(file_name, algo=svd)