Python影视数据分析可视化

# ✨基于影视数据的分析与可视化展示系统✨ data_analyse分支，用于数据分析 ## Movie_recommender ### 1 功能接口 recommender.py ```python if __name__ == '__main__': test = recommender() # test.movie_recommender("Avengers: Infinity War - Part I (2018)", 5) # test.user_recommender(2, 5) test.user_keywords(2,'spy hit strike hero war death soldier army') ``` 算法为KNN，提供三种不同的KNN模板算法 ```python # 分别为使用自定义标准的KNN算法（0），考虑均值的KNN算法（1），考虑均值和方差的KNN算法（2） sim_options = {'name': 'pearson_baseline', 'user_based': False} if mode == 0: self.algo = KNNBaseline(sim_options=sim_options) elif mode == 1: self.algo = KNNWithMeans(sim_options=sim_options) elif mode == 2: self.algo = KNNBasic(sim_options=sim_options) else: exit(0) ``` ### 2 根据电影标题推荐 personal_recommender/KNN_movie.py ```python if __name__ == '__main__': test = Movie_KNN_recommender() result = test.recommend(122912, 10) for i in result: print(i.values[0]) ``` ### 3 根据用户推荐 personal_recommender/KNN_user.py ```python if __name__ == '__main__': test = Movie_KNN_recommender() result = test.recommend(122912, 10) for i in result: print(i.values[0]) ``` personal_recommender/test.py ``` 组织train.csv和test.py ``` ### 4 根据用户和关键词推荐 personal_recommender/User_Keywords.py ```python if __name__ == '__main__': test = KNN_usr_keywords_ensemble() result = test.recommend(2,'spy hit strike hero war death soldier army') for i in result: print(i) ``` 实现原理为在 3 的基础上，提取关键词和简介词汇，按照linear_kernel拟合