ohjho / recommendation_system

Xccelerate Data Science Bootcamp Collaborative Project: 4 flavours of recommendation systems

recommendation_system

Xccelerate Data Science Bootcamp Collaborative Project: 4 flavours of recommendation systems using the Booking Crossing Dataset which is also included here in this repo.

See the project's details here

How to Use this repo

1. Clone this repo:

$ git clone https://github.com/ohjho/recommendation_system.git
$ cd recommendation_system

2. install the requirements. We highly recommend doing this inside a virtualenv and avoid dependency hell.

#---------------- optional ------------------
$ mkvirtualenv --python=`which python3` NameOfYourEnv
$ workon NameOfYourEnv
#--------------------------------------------

(NameOfYourEnv) $ pip install -r requirements.txt

and just check and resolve any packages dependency issues if they show up under pip check. It should say No broken requirements found.

3. Start Jupyter notebook

$ jupyter notebook

Data Cleaning

How to use data_cleaning.py

The script data_cleaning.py will import the datasets and clean the data.

To get 3 separate dataframes, do this

from data_cleaning import get_clean_data
df_books, df_users, df_ratings = get_clean_data()

And if the csv files are not under data/, use the path argument.

To get one merged dataframe, do this:

from data_cleaning import get_merged_data_frame
df_merged = get_merged_data_frame(user_argv=user_threshold, isbn_argv=book_threshold)

where user_threshold is the threshold to filter out users with fewer than this number of books rated. books_threshold is the books counterpart And if the csv files are not under "/data/", use the path argument.

Modeling

A. Content-based Filtering

B. Collaborative Filtering

C. Latent Factor Analysis

Presentation

Google Slides