simple readme and requirements

Books/03_book_to_user_converter.ipynb

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Books/2.6_cluster clustering with recs.ipynb

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Data/Books/Recommended Storage/title_users_test.npy

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Data/Books/Recommended Storage/title_users_train.npy

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Data/Books/Recommended Storage/title_users_val.npy

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README.md

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 ---
 
+# Books Recommendation Project (BT5153)
+
+Hello, and welcome to our books recommendation project for BT5153!
+
+# Project Directory
+## Source Code
+Codes are stored under `./Books` as `.ipynb` files, and named according to the order they should be run. 
+
+## Data
+Data used for the project is stored in `./Data`. 
+
+Raw data, retrieved from the Goodreads dataset [here](https://mengtingwan.github.io/data/goodreads.html), can be found under `./raw-data`. 
+
+For our submission, we have created a representative subset of our dataset to be included in the zip submission, and can be found in `./Data-sub`.
+
+# To run our project in Windows:
+
+## Create a virtual environment (optional) 
+Run these commands:
+1. `python -m venv venv`
+2. `venv\Scripts\activate`
+3. `python -m pip install -r requirements.txt`
+
+## Locate python notebooks
+All python notebooks can be found in the subdirectory `./Books/`.
+
+## Data preprocessing
+Run all cells in the file `1_data_split.ipynb`.
+
+## Generating recommendations
+Run all cells in the following files:
+* `2.1`
+* `2.2`
+* `2.3`
+* `2.4`
+* `2.5`
+* `2.6`
+
+Then, run the following file to generate recommendation for users:
+`3_book_to_user_converer.ipynb`
+
+## Ensemble model
+Run this file: `4_ensemble.ipynb`
+
+----------------------------------------------------------------
+# Project Description
+In response to the overwhelming number of book choices online, which often leads to decision paralysis and wasted time, we propose the implementation of a Natural Language Processing (NLP) powered recommendation system to address this challenge.
+
+
+For full project description, see the report file in submission.
+
+## Members:
+* Ang Kai En (A0221945E)
+* Meritxell Camp Garcia (A0280366B)
+* Sidharth Pahuja (A0218880X)
+* Sim Jun You (A0200198L)
+* Sim Yew Chong (A0189487A)

requirements.txt

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+pandas
+numpy
+matplotlib
+torch
+transformers
+tqdm
+scikit-learn
+gensim
+contractions
+langdetect
+nltk
+tiktoken
+langchain==0.1.11
+faiss-cpu
+ipython
+ipykernel