63 lines
1.4 KiB
Markdown
63 lines
1.4 KiB
Markdown
<<<<<<< HEAD
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# Python_AI_Projekt
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Projekt för Pythonkurs (AI)
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Autoencoders
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Scikit-learn: - K-nearest neighbors
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=======
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# Supervised Learning - Movie/TV-Show recommender
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## Specification
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Movie/TV-Show recommender
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This program will recommend you what movie or th-show to view based on what Movie/TV-Show you like.
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### Data Source:
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I will use 4 datasets from kaggle, 3 datasets from streaming-sites Netflix, Amazon Prime and Disney Plus, also 1 from a IMDB dataset.
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### Model:
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I will use k-Nearest Neighbors (k-NN) alhorithm that can help me find other titles based on features like Title, Release year, Description, Cast, Director and genres.
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### Features:
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1. Load data and preprocessing before creating new dataset csv file.
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2. Model training with k-NN algorithm.
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3.
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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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* Release year
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* Cast
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* Director
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* Description
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2. User data:
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* What Movie / TV-Show
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* What genre
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* Director
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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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* numpy: numerical operations
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* beatifulsoup4: web scraping
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### Classes
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1. LoadData
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* Loading, cleaning and saving alla data to csv
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* check_data
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* clean_text
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* clean_data
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* load_dataset
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* create_data
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* save_data
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* load_data
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2. UserData
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* input
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3. Recommendations
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* get_recommendations
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