57 lines
1.6 KiB
Markdown
57 lines
1.6 KiB
Markdown
# Supervised Learning - Movie/TV-Show recommender
|
|
|
|
## Specification
|
|
Movie/TV-Show recommender
|
|
|
|
This program will recommend you what movie or th-show to view based on what Movie/TV-Show you like.
|
|
You should be able to search for recommendations from your Movie/TV-Show title, cast, director,
|
|
release year and also Description, and get back a recommendations with a explanation on what just this
|
|
title might suit you. It will get you about 25 recommendations of movies in order of rank from your search,
|
|
and the same from TV-Shows that will match the same way as Movies.
|
|
|
|
### Data Source:
|
|
I will use 4 datasets from kaggle, 3 datasets from streaming-sites Netflix,
|
|
Amazon Prime and Disney Plus, also 1 from a IMDB dataset.
|
|
|
|
### Model:
|
|
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.
|
|
|
|
### Features:
|
|
1. Load data from several data-files and preprocessing.
|
|
2. Model training with k-NN algorithm.
|
|
3. Search with explanation
|
|
|
|
### Requirements:
|
|
1. Title data:
|
|
* Title
|
|
* Genres
|
|
* Release year
|
|
* Cast
|
|
* Director
|
|
* Description
|
|
2. User data:
|
|
* What Movie / TV-Show
|
|
* What genre
|
|
* Director
|
|
|
|
### Libraries
|
|
* pandas: Data manipulation and analysis
|
|
* scikit-learn: machine learning algorithms and preprocessing
|
|
* beatifulsoup4: web scraping (if necessary)
|
|
|
|
### Classes
|
|
1. LoadData
|
|
* check_data
|
|
* clean_text
|
|
* clean_data
|
|
* load_dataset
|
|
* create_data
|
|
* save_data
|
|
* load_data
|
|
2. UserData
|
|
* input
|
|
3. Recommendations
|
|
* get_recommendations
|
|
|