TV-Show-recommender/README.md

Supervised Learning - TV-Show recommender

How to run program

Before running program

First thing to do is to extract TMDB_tv_dataset_v3.zip in dataset folder so that it contains TMDB_tv_dataset_v3.csv.

Running program

Start main.py and it will load dataset and ask for a title to get recommendations from, also how many recommendations wanted. Then enter and you will have those recommendations presented on screen.

Specification

TV-Show recommender

This program will recommend you what tv-show to view based on what you like. You will tell what tv-show you like and how many recommendations wanted, then you will get that amount of recommendations of tv-shows in order of rank from your search.

Data Source:

I will use a dataset from TMBD

https://www.kaggle.com/datasets/asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows

Model:

I will use NearestNeighbors (NN) alhorithm together with K-NearestNeighbors alhorithm.

Features:

1. Load data from dataset and preprocessing.
2. Model training with NN & k-NN algorithm.
3. User input
4. Recommendations

Requirements:

1. Title data:
   • Title
   • Genres
   • First/last air date
   • Vote count/average
   • Director
   • Description
   • Networks
   • Spoken languages
   • Number of seasons/episodes
2. User data:
   • What Movie / TV-Show prefers
   • Number of recommendations wanted

Libraries

• pandas: Data manipulation and analysis
• scikit-learn: machine learning algorithms and preprocessing
• scipy: A scientific computing package for Python
• time: provides various functions for working with time
• os: functions for interacting with the operating system
• re: provides regular expression support
• textwrap: Text wrapping and filling

Classes

1. LoadData
   • load_data
   • read_data
   • clean_data
2. ImportData
   • load_dataset
   • create_data
   • clean_data
   • save_data
3. TrainModel
   • train
   • recommend
   • preprocess_title_data
   • preprocess_target_data
4. UserData
   • input
   • n_recommendations
5. RecommendationLoader
   • run
   • get_recommendations
   • display_recommendations
   • get_explanation
   • check_genre_overlap
   • check_created_by_overlap
   • extract_years
   • filter_genres

References

• https://scikit-learn.org/dev/modules/generated/sklearn.neighbors.NearestNeighbors.html
• https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
• https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html
• https://scikit-learn.org/0.16/modules/generated/sklearn.decomposition.TruncatedSVD.html
• https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.hstack.html
• https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html