101 lines
3.0 KiB
Markdown
101 lines
3.0 KiB
Markdown
# Supervised Learning - TV-Show recommender
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## How to run program
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**Before running program**
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First thing to do is to extract TMDB_tv_dataset_v3.zip in dataset folder so that it contains TMDB_tv_dataset_v3.csv.
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**Running program**
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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.
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## Specification
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**TV-Show recommender**
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This program will recommend you what tv-show to view based on what you like.
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You will tell what tv-show you like and how many recommendations wanted, then you will get that
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amount of recommendations of tv-shows in order of rank from your search.
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### Data Source:
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I will use a dataset from TMBD
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https://www.kaggle.com/datasets/asaniczka/full-tmdb-tv-shows-dataset-2023-150k-shows
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### Model:
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I must first preprocess data with vectorization so that i can train it in NearestNeighbors (NN) alhorithm with cosine distance. Later use NearestNeighbors (NN) in combination with K-NearestNeighbors (K-NN) alhorithm.
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### Features:
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1. Load data from dataset and preprocessing.
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2. Model training with NN & k-NN algorithm.
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3. User input
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4. Recommendations
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### Requirements:
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1. Title data:
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* Title
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* Genres
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* First/last air date
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* Vote count/average
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* Director
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* Description
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* Networks
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* Spoken languages
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* Number of seasons/episodes
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2. User data:
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* What Movie / TV-Show prefers
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* Number of recommendations wanted
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### Libraries
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* pandas: Data manipulation and analysis
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* scikit-learn: machine learning algorithms and preprocessing
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* scipy: A scientific computing package for Python
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* time: provides various functions for working with time
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* os: functions for interacting with the operating system
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* re: provides regular expression support
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* textwrap: Text wrapping and filling
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### Classes
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1. LoadData
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* load_data
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* read_data
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* clean_data
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2. ImportData
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* load_dataset
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* create_data
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* clean_data
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* save_data
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3. TrainModel
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* train
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* recommend
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* preprocess_title_data
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* preprocess_target_data
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4. UserData
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* input
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* n_recommendations
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5. RecommendationLoader
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* run
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* get_recommendations
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* display_recommendations
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* get_explanation
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* check_genre_overlap
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* check_created_by_overlap
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* extract_years
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* filter_genres
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### References
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* https://scikit-learn.org/dev/modules/generated/sklearn.neighbors.NearestNeighbors.html
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* https://scikit-learn.org/1.5/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html
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* https://scikit-learn.org/dev/modules/generated/sklearn.preprocessing.StandardScaler.html
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* https://scikit-learn.org/0.16/modules/generated/sklearn.decomposition.TruncatedSVD.html
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* https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.hstack.html
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* https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
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